Speech to text app can be termed as an assistive technology that is meant to enable individuals and businesses with faster and precise transcription. It is also referred to as assistive technology since it is being used by students and people with writing difficulties. Over the years, the technology has matured greatly and today it is being widely used across various industries and in various roles.

With ever-expanding integration of speech recognition or voice recognition technology in commercial roles, companies have rushed in to develop their own version of the speech to text devices; most of which are actually based on Google’s speech technology.

Well, in this blog we will be looking at the assistive role of natural language processing technology and see how different speech to text devices are empowering individuals and students with disabilities to excel and produce better results.

Is there an App that Converts Speech to Text Using a Headphone Device?

As mentioned in the beginning, the rapid adaptation of speech recognition technology within different commercial and assistive functions has led to the development of various speech to text devices that come with characteristic features and utility tools.

Today, students with writing disabilities are allowed to use speech to text devices as assistive technology for learning and studying purposes. In fact, students without any writing disability also frequently turn to technology as a replacement for typing long assignments. Converse Smartly® is one such device that is empowering students to get over with their educational needs.

Converse Smartly® is a proprietary text to speech software developed by Folio3, offering a technological alternative to individuals who find typing very stressful. The speech to text device from Folio3 is an intelligently developed tool that eliminates the need for typing lengthy documents altogether; adding an element of convenience for users. With the strong algorithms powering the device, the tool can produce high-quality and precise transcription results to eliminate the hassles of typing.

Free Speech to Text Software - Efficient, Quick, and Accurate

What makes Converse Smartly® free speech to text software, a cutting edge speech to text device is the use of cutting-edge technology powering the tool from the backend. It is a reliable and effective speech to text technology that can easily be integrated with Android, iOS, and Windows-powered devices. The only prerequisites to use the tool are high-quality (preferably noise cancellation) headsets and a strong internet connection, and you are ready to roll.

Some of the amazing features of Converse Smartly® you can relish using a high-quality headset and strong internet connection includes:

• Multi-lingual transcription services (support for English, Spanish, and German languages)

• Intelligent technology capable of analyzing speech patterns of people

• Capability to understand the speech content

• Multi-speaker voice detection; making it ideal for seminars, conferences and groups meetings

• Capability for sentiment analysis

• Strong algorithms for identification of keywords and topics

• Offers automatic speech summary

Apart from the above-mentioned features of the device, Converse Smartly® brings a whole set of other features that makes it an appealing tool for students and businesses alike. Let’s see in some details some of the advanced features offered by the tool:

Speech Analysis

The device is able to analyze the speech patterns of people to be able to get better understand of the content of the speech and produce more precise and contextual transcription

Text Analysis

This is an advanced feature powered by powerful algorithms that enable an analysis of textual content and presenting it as data, which subsequently can be used for data mining or to gain insights into the data patterns.

Multiple Speaker Detection

While many speech to text devices struggles to identify voices from multiple speakers, the robust technology powering the Converse Smartly® enables it to identify multiple speakers and create homogenous segments as per the speakers’ identity.

Live Audio Transcription

Converse Smartly® also enables the automatic real-time transcription of speech into the text from the English Language even from comparatively lower quality audio and different audio formats.

How Speech to Text can Help Students/Individuals with Disabilities?

Speech recognition technology is its assistive role can assist students/individuals with disabilities in various ways, as it resolves the spelling problems. By leveraging the technology individuals with learning disabilities are no longer required to correct or generate spelling of words, rather all they need is to pronounce the word clearly and let the technology to the magic for them. Here are some ways speech to text devices are taking over as assistive technology to help individuals with disabilities:

- By eliminating the need to generate the correct spelling, it assists individuals/students with a learning disability to keep their focus on the content of the document and not worry about the integrity of the text, which greatly improves the quality of the content

- It has been proved that by dictating your thoughts, individuals/students are able to produce much better content as opposed to traditional typing. The technology enables the speakers to completely focus on the context and fluency of the content and not worry about the grammar or spellings; thereby producing much better quality content.

How Can I Access Voice Typing?

To access Voice Typing, you have three requirements:

Firstly, you need to have a strong and reliable internet connection

Next, you got to have the latest web browser (Chrome preferably for Google Speech)

Lastly, you should have a microphone and headset attached to the device

How Speech to Text Device Technology Can Help With Student Writing

As discussed above, the speech to text devices aren’t just great for students with learning disabilities but generally for all students looking to make their lives easier by getting away from typing lengthy assignments.

Students can now leverage the voice recognition technology to dictate their way through the assignments in a much faster and accurate manner. Also, the technology is great for essay writing or research work, as it helps students to keep their focus strictly on the quality of the content and not worry about grammatical, spelling, or writing errors. Today, students can get access to speech to text devices not just on their computers but also on their android or iOS devices.

How Folio3 Speech to Text Technology Could Help Students with Disabilities and Special Educational Institutes

Converse Smartly® from Folio3 is one of the leading speech to text software that has been developed using cutting-edge technology. The software is designed to offer users high-utility tools to enable them with ease of us. Powered by strong machine learning, artificial intelligence, and natural language processing algorithms, the software offers various features and tools for students with disabilities to talk through their typing needs much faster and more efficiently.

What is the best speech to text app for students?

Google Speech to text engine and Converse Smartly® are two of the leading speech recognition software for students. Both of the platforms come with higher accuracy, along with various automation features and high-utility tools that enable students to produce better content.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at [email protected]

Speech to text software is the advanced technological solution to the transcription services – making it easier and cheaper than even to transcript just like you have always wanted. But, is technology really that efficient? And how the technology behind speech to text work, anyway?

Well, to put it simply, the speech to text software or what is also referred to as speech recognition technology is computer programs that leverage linguistic algorithms, along with the AI and machine learning technologies to be able to listen, understand, and responds to the auditory signals by converting them to words using Unicode characters. And to put it for a normal un-technical person, the speech to text software or voice recognition technology simply “listens” to your voice and give you the editable transcript of it.

Over the years the technology has greatly improved in its accuracy, as well as, features. Today, various tech companies have launched their dedicated version of speech recognition software. Most of these speech to text software works online, and quite a few of them is based on Google’s speech to text technology. The service is packaged by companies based on the pricing factor, as well as, some unique features that make them attractive to targeted clients. In general, the technology costs around £0.10 for per minute transcription of recorded voice, to make things even more interesting is that some software offers free services; but you can only guess their accuracy for that matter. In terms of accuracy, the most advanced and well-reputed speech to text software available in the market today are able to give 90 to 95% accuracy, however, the accuracy of the transcript text will depend on the “cleanliness” and “quality” of the recording, which undoubtedly is one of the most crucial aspects when you are looking for accurate transcription service from any voice recognition software.

Now, in this blog, we will be looking at some of the best speech to text software available in the market, as well as, discuss some of the good, bad, and ugly truths of the technology. So, without wasting time, let’s get started.

What is Converse Smartly Speech to Text Software?

So, our first choice of voice recognition software is Converse Smartly® which has quickly gained fame and popularity amongst businesses and individuals alike for its superior features and high accuracy. The software is targeted towards organizations and individuals alike; equipped with features that make it easier and faster for users to leverage transcription service with higher accuracy. The best part about Converse Smartly® is the fact that it is able to detect multiple voices, which makes it an ideal choice for team meetings, conferences, and seminars.

What makes Converse Smartly® a well-established speech to text software is the advanced natural langugaes processing technology that goes behind it. Developed by experts at Folio3, the software packs strong linguistic, machine learning and deep learning algorithms for greater accuracy, coupled with high-utility tools and features to make it more efficient and productive for users.

By integrated intelligent machine learning algorithms, Converse Smartly® is able to improve its efficiency and accuracy over time, by learning and adapting to the environment. The software is able to automatically identify multiple voices and perform sentiment analysis, as well as, highlight themes and objectives. Some of the key features of the software include:

- Speech Analysis

- Text Analysis

- Multiple Speaker Detection

- Live Audio Transcription

Some of the other notable features included in Converse Smartly® speech to text converter includes:

- Automatic summary generation

- Word cloud generation from input speech and text

- Perform sentiment analysis

- Identification of key entities and themes

- Multiple language support (English, Spanish, German, French)

- Technologies used: Google Speech and IBM Watson

What is Speech to Text Accommodation?

Speed to text accommodation referred to special cases where students are allowed to use the speech to text software for testing and other purposes. Many schools and testing systems allow speech to text accommodation for students with special needs, or as part of their IEP plans.

What are the Pros and Cons of Speech to Text?

Ok, now that we are done with some of the leading voice recognition technologies available in the market, it’s time to assess the good, bad, and the ugly of the technology. So, let’s just get started straight away.

The Good

The most obvious and significant benefit of speech to text software is the speed and affordability of the technology. As said above, the technology has matured greatly over the years and today offers over 90% accuracy for transcription services (based on the quality of the voice). Also, technology can be used for real-time transcription services and has the capacity to detect multiple voices, which makes it ideal for conferences, seminars, team meetings, and other business needs.

Also, the technology isn’t just incredibly fast and responsive, it’s also quite affordable for the liking of most businesses. The cost may vary from software to software and the choice of package, however, it is generally within affordability ranging from $0.5 to $0.9 per minute for automatic conversion.

Speech to Text Software: The Bad

Perhaps, the biggest limitation of the technology (yet) is that in most cases it offers an only verbatim text only. This means that without the presence of humans, the transcribed text may end up with a low readability score. For instance, during speeches, we frequently pause or make different noises like “erm” and other words. And since the text produced by the technology is verbatim it will include every noise or word it would hear. Thereby, often there is a need for a human transcriber to reread the text and clean any unwanted words to increase the readability of the text.

What is the Best Speech to Text Case Studies?

3Play Media integrated voice recognition technology to improve its captioning service 

Captioning isn’t the easiest task for video content creators. However, now, 3Play has partnered with a leading speech to text software solution providers to improve the accuracy of captioning for its audience for its broadcast content.

Full Fact partnered with speech recognition technology provider for independent fact-checking

False news and fake content have become a menace for the media industry, which struggles to present credible and authentic content to the public. Now, Full Fact has partnered with a voice recognition technology provider to help news agencies perform fast and accurate fact-checking of news.

Transcriptive partnered with speech to the text service provider to create more efficient video workflows

There is an ever-increasing demand for video, broadcast, and films. This means a greater need for streamlined and efficient video production workflows. To ensure fast and seamless addition of subtitles and metadata into the video content, Transcriptive have integrated speech to text technology to develop an efficient and productive workflow.

What is the speech to text technology called?

Speech to tech technology is referred by various names including dictation technology, voice to text technology, and speech recognition technology.

How do I speak to text in a word for free?

There are various software that offers free transcription services for users. While these free tools may be great for individual needs, the accuracy issues with these tools don’t make them the perfect fit for commercial usage.

How do I improve my voice to text on mobile?

The most recommended tip to improve the voice to text in mobile is the use of high-quality headset microphones, which keep the microphone at a static position directly in front of the mouth.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at [email protected]

The world was left in awe back in 2012 when Google announced that it has succeeded in developing the first-ever artificial intelligence models that are able to identify cats on YouTube. Now, today this might not come as shocking news it may be boring to many people; however, back in those days when artificial intelligence was still in early development stages, this was a big breakthrough and only represented the endless potential that was waiting to be unlocked.

Today, as we all are too complacent in using facial recognition technology on our mobile phones, it just represents how much artificial intelligence and other futuristic technologies have developed. And if you haven’t already thought how facial recognition software works, perhaps it is time to consider the technological marvel which you are too complacent to use every day and not think about once.

Face scanners or facial recognition technology is basically the most powerful (Yet) representation of the capabilities of artificial intelligence solution, deep learning, and other similar technologies, which have been developed over the decades to be able to offer users these technological marvels.

Here in this blog, we will be looking at how does facial recognition software works, as well as, look into some of the applications of the facial recognition system in different fields.

So, let’s get started…

What is a Facial Recognition System?

Facial recognition system or face scanners is an advanced application of artificial intelligence technology that is capable of identifying a subject through an image, video, or any other visual element of the face. The most common use of face scanner technology is in our smartphones, which we use every day.

Going a little deeper, the facial recognition system is just another biometric verification system like a fingerprint scanner, voice recognition, IRIS, and others, where AI-based algorithms are able to analyze facial features of a subject to identify and verify its authenticity. However, while identification of fingerprints through fingerprint scanners is much easier, face recognition is much more complicated; since it has to calculate the crucial threshold of similarity between various factors of a face to confirm the authentication of the subject.

Couple it with other complex facial features like wearing hats, beards, and other elements, and facial recognition is surely much difficult and complex as compared to other biometric verification systems including fingerprints and iris scanners; where we have definite patterns to match.

Why Facial Recognition Software is the Talk of the Town in 2020?

Facial recognition has been the talk of the town in 2020; given its mainstream use in smartphones. In fact, the technology was already well established in the smartphone industry back in 2019, however, it only further established its position as the preferred biometric verification technology for smartphone users.

Today, facial recognition is being used across various applications, which required massive image and video datasets to get the machine learning algorithms used in the technology trained to the point that they are able to meet the incredible demand of the technology across various AI products.

Tech companies are increasingly developing rigorous and high-utility applications using facial recognition technology including cattle and livestock industry, traffic analysis, AI video drones for terrain mapping, and many others.

Apart from private applications, facial recognition technology is also being used by governments and law enforcement agencies to identify criminal suspects for easier and better prosecution. Microsoft has in fact also developed AI emotion analysis which can be used to perform advanced sentiment analysis for the projects.

Overall, the sheer power of machine learning and AI products has just started to unveil and we can certainly expect a lot more applications of the technology in the coming years.

How Does Facial Recognition Software Work for Road Traffic Analysis?

Road traffic analysis is just one of the various advanced applications of facial recognition software, where the application is being used actively to monitor and analyze road traffic for better control and easier management.

The advanced facial recognition expertise at Folio3 has resulted in the development of one of its kind AI-powered Road & Safety solutions. The application is powered by advanced deep learning technology and enables easier analysis of road and traffic situations. Leveraging its powerful advanced deep learning algorithms, the application is able to count vehicles on the roads while segregating them into the vehicle type for easier classification of vehicles along with the total count. By using clean and robust algorithms, the application is able to give maximum accuracy and top performance.

This is just one example of the power and potential of facial recognition applications, which will become a necessary service for better management of cities especially congested urban centers. The application is ideally suited for remote management and surveillance of traffic on motorways. Similar applications may include the video surveillance software which can be integrated with the road traffic analysis software for a robust traffic management ecosystem.

How Does Facial Recognition Software Work for Ai Video Analytics?

Another high-potential area for application of facial recognition technology is the video analytics services and solution, which can be used to reinforce the security and surveillance of cities and urban centres. Again, companies like Folio3 has been offering comprehensive and accurate services and solutions for video analytics, which are marketed as effective monitoring systems, as well as, technology for operational efficiency. The technologies behind video analytics solutions include computer vision and machine learning, which are able to create a sense out of any video footage.

Some of the potential applications of advanced video analytics software backed by computer vision, machine learning, and neural networks include;

Facial recognition for smart access.

Pipeline management system.

Customer sentiment analysis.

Advanced driver assistance systems.

Farms and Livestock management.

Terrain mapping.

Medical imagery analysis.

Retail footfall monitoring.

Smart traffic/transport system.

Smart security solutions.

How Does Facial Recognition Software Work for Cattle and Livestock Industries? 

Drones are the perfect match for AI technologies. The two technologies have been integrated for the development of various applications that have in fact helped to solve real-world problems. Now, with the availability of programmable AI drones, tech companies have been able to create applications that are being used for robust farm and livestock management; offering ranchers easy and remote management for surveillance and management of farms and livestock.

Can AI facial recognition software be fooled?

The facial recognition technology has matured quite a bit to the point that it’s pretty hard to fool a robust AI facial recognition software. However, still there are loopholes and limitations at present which can be exploited to fool AI facial recognition software.

How accurate is facial recognition software?

As said above, facial recognition technology has matured greatly with high near-perfect accuracy. Various face identification algorithms can have accuracy scores of up to 99% or even higher.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at [email protected]

Writing is a gratifying activity, at least for people like me who love to share their thoughts. However, if it’s even better when you don’t actually have to type to write and rather may simply pull out your phone and start dictating your thoughts and let the magic happen. I mean who wouldn’t like to just lay-back on the couch with legs comfortable up and just dictate lengthy notes?

Well, thanks fully with the advancement of the google speech to text technology mean that now we can keep taking any amount of voice notes on the go through our android phones, using the speech to text android technology. Here in this blog, we will be briefly looking into the speech to text android technology; how you can enable it on your smartphone, and what are some of the best Ai speech to text android apps that you can start using right away.

So, let’s get started.

How to Send Speaking Text Messages Using Speech to Text Android Feature?

Well, obviously before we began looking at some of the speech to text android apps that you can use right away, you should know some basics of using the technology.

And while this feature may seem basic to many android users, still for new users that have just switched to the Android ecosystem or people who aren’t much tech-savvy, a quick guide on how to turn on speech to text on android or how to send a text message using the android speech to text is always helpful, right?

So, let’s see how to turn on speech to text on android and send text messages.

How To Text Message On Android Hands-Free

Step 1 – Go to your message app and start composing

Whichever messaging app you are using (the default or Google messages or any other third-party app) just open the app and start composing the message. Once you are in the composing message screen, you would see a “Microphone” icon at the bottom right of the keyboard.

Tap the icon and start to speak!

Just tap the button and you will see the “Speak Now” message; and you are done. Just start saying whatever you want to write and let the android speech to the text do the magic for you.

Now, for those of you who aren’t used to dictation through Google Speech to text, you may want to learn some dictation punctuation to get started (theses aren’t that difficult though). Nonetheless, you can easily find some of the most common punctuation like to place the “Period” mark you would have to literally say “Period”.

Step 3 – Confirm and Send

Once you are done dictating, just review the message to be sure it’s correctly transcribed and simply press the “Send” button to let it fly away to its destination.

How to Turn On Speech to Text on Android Devices?

Ok, now that you know how to send a text message using voice typing on android, let’s take a quick look at how to turn on speech to text on android devices.

Step 1: Checking the device settings

Before you turn on the voice typing option, you need to check the device settings.

Here’s are a few things you need to check and confirm:

Required:

Remember, voice typing only works with Android 5.0 or above, which means you should check out the Android version on your smartphone.

You would require the latest version of Google app, which is the default for the latest version of android or you can update it from Google Play.

Recommended:

Detect “Ok Google” from any screen.

For offline speech to text, you should download the language of your choice.

Note: Google's speech to text is available in multiple languages including; English, French, German, and others.

Step 2: Download Voice Access

Once you are done with checking with the prerequisites and recommendations, you would need to download the voice access from Google Play.

Step 3: Enjoy using Voice Access

Well, that’s all is required for you to get the magic started. Here some few simple steps you may follow to turn on Voice Access:

Go to your phone’s settings.

Click on the “Accessibility”.

Tap the “Voice Access”.

At the top right corner “Slide/Switch” On/Off the Voice Access.

Say Ok Google and speak a command like “Open Chrome” and you are on your way.

6 Best Free Android Speech to text Dictation Apps

Now that we are done with the basics of speech to text for android, let’s just quickly jump on some of the best speech to text app for android. We will start by looking at some of the free speech to text apps and then move up to some paid apps.

1. Converse Smartly®

Pros

Quite accurate.

Intelligible.

Adaptive.

Converse Smartly® has gained much appreciation and popularity as one of the best speech to text app for android. The app is developed by Folio3 and offers advanced speech to text features to assist you to perform all of the major tasks over your phone without having to actually touch the keyboard.

The best part about Converse Smartly® is its use of advanced technologies including artificial intelligence and machine learning that not only make it super accurate to transcribe the speech, but also give it the power to learn over time and further improve the accuracy. The developers at Folio3 have certainly done a commendable job using their expertise not just by making a super accurate and advanced speech to text software but also by keeping it intelligible and easy to comprehend for users. Once you are a few weeks into using the app, you will be producing 99% accurate write-ups using your voice. The Converse Smartly® platform is fully compatible with the latest android versions.

2. Google Keyboard

Pros

Free.

Easy to use.

Additional features.

Cons

No shortcut commands.

Well, Google Keyboard is certainly right at the top when it comes to the best speech to text apps for android. While the majority of the Android devices will already have the Google Keyboard accessible, if it’s not available you can always download it from Google Play. And while Google Keyboard is primarily meant as a physical input app (as can be taken by name) it also offers a speech input option directly. And when it comes to the power of Google behind it, you can very well comprehend the power and responsiveness of the tool.

And just if the accuracy and power of the tool weren’t enough, the app offers a plethora of other advanced features (you always expect something out of the box from Google) like triggering the images through voice commands. Furthermore, the app can support over 60 languages and is compatible with Google translate.

3. Speech notes

Pros

Powered by Google Voice recognition technology.

Recognize punctuations.

Easy to use.

Cons

No iOS app.

Next in line, we have a rather easy to use dictation app; Speechnotes. What I like the most about the app is that it doesn’t require you to make an account or anything to get started. Once you are done downloading, simply open the app, press the “Microphone Icon” and you are ready to go.

What makes it even better is that it used the Google Voice Recognition technology, which guarantees the utility and accuracy of the apps. To make life a little simpler for users, it enables easy dictation for punctuation marks, and even offers a keyboard for punctuation.

4.Voice Notes

Pros

Great for quick notes.

Reminder feature.

Organizational features.

Cons

Not great for lengthy notes.

Well, our next choice for the best speech to text apps for android is a smart app that’s perfectly developed to support speech to text conversion on the go. While some of the other apps like the Converse Smartly® or Speechnotes is great for lengthy dictation; the Voice Notes is definitely the go-to app when you are looking to send quick small notes on the go.

The app not only lets you transcribe the voice message quickly and accurately, but you can also save the audio message for later. Also, Voice Notes offer pretty impressive organizational features like reminder tools, which enable users to create an alert for anything or even set recurring reminders. Some of the other tools that come with this nitty tool include customizable categories and color tags for better management.

5.Speech Texter

Pros:

Uses Google’s database.

Online and offline features.

Custom library.

Last, in line, we have yet another powerful and robust android speech to text app that works impeccably well both online and offline. Again, what makes this app special is its use of Google’s database, which means you can certainly rely on its accuracy and powerful features. Also, if you want to use the app in offline mode, you would have to re-download the language library from Google Play. You can easily download the language library of your choice by heading towards the virtual keyboard following these steps:

Settings > system > languages and input > virtual keyboard

Once you are there, simply switch the Google Voice Typing “On” and choose “Offline Speech Recognition”. From there, you can choose the language of your choice.

Apart from the speech to text dictation, the app also lets you create and send messages, emails, or tweets using voice input.

5 Best Paid Speech to text App for Android

Now that we are done with the best free speech to text apps for android, let’s look at some of the best-paid android speech to text apps.

1. Dragon Anywhere

Pros:

Great accuracy.

easy sync with the desktop version.

Cons:

Limited dictation within the app.

Need internet connection.

Dragon Anywhere is undoubtedly one of the best Android speech to text apps available in the market. It’s a comprehensive app that offers robust dictation capabilities which are powered by the cloud. This means that you can are essentially able to get consistent experience and access to the notes from your mobile device or desktop software.

2. Otter

Pros:

Free tier.

Export options.

Team collaboration.

Live captioning.

Otter is another powerful cloud-based speech to text android app. What differentiates it from the Dragon Anywhere is that it is essentially meant to be used in mobile devices like smartphones or laptops. The app offers consistent, accurate, and real-time transcription services, as well as, enables users to edit and organize the notes, as and when required.

Perhaps the best part about the app is that it is meant to support meetings, lectures, or other team collaborations. This means that it’s the perfect business app that can be used for business meetings or during class lectures with multiple speakers.

3. Verbit

Pros

AI-powered.

Team working.

Enterprise service.

Cons

Not always live.

Verbit using AI capabilities to offer smart and reliable transcription and captioning services to users. While it may be used by individuals, it is primarily targeted towards the educational institutes and enterprises. By using advance and complex neural networks and algorithms, the app can suppress the background noise and concentrate on the speaker, thereby, greatly improving the accuracy and speed of the transcription. And while it does offer a speech to text capabilities, it is best described as a transcription app focused one educational institute and organizations.

4. Speechmatics

Pros:

Keyword triggers.

Media Captioning.

Support multiple accents.

Next in line, we have another powerful speech to text app – speechmatics. Like some of the other apps in the list, speechmatics use power technologies like machine learning to build the app accuracy over time and offer a reliable and precise speech to text services to users.

The best part about the app is its capability to comprehend multiple accents and keep up with its accuracy; which is great considering how many users suffer because of unintelligible accents.

6. Braina Pro

Pros

Robust digital assistant.

Great accuracy.

Cons:

Subscription model only.

Braina isn’t just a powerful speech to text application, rather it’s a robust and all-round digital assistance that helps you complete all major tasks on your mobile or PC. The app supports the third-party dictation in over 90 languages, which certainly take it to the top as the best android app for speech to text conversion.

And since it’s your virtual assistant, you can use it for a plethora of daily routine tasks like setting reminders, alerts, searching music, or any other tasks.

Folio3 is your best speech to text android custom app partner

Folio3 is known for its innovative and practical technology solutions that are meant to assist businesses to achieve their full potential. Converse Smartly® is the trademark speech to text software that has proven the deep expertise of the company in the application of futuristic technologies like machine learning, deep learning, and artificial intelligence to come up with precise and versatile technology solutions. Converse Smartly® is meant to offer businesses and individuals alike with a simple to use and interactive speech to text conversion platform which can help them to improve their workflow processes and systems and improve their operational efficiency.

How do I activate voice to text on android version 10?

To activate the voice to text on android version 10 follow these simple steps:

Install Gboard on your mobile device (can be downloaded from Google Play).

Open any typing app like message or Gmail.

Start composing the message.

Click on the “Microphone Icon” at the bottom right.

Start speaking once you see “Speak Now” message.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at [email protected]

The simplest definition of audio transcription is the conversion from verbal to written form. However, this isn’t just limited to it, and once you start working your way through the audio transcription you will surely discover new things. The audio transcription enables you to carve out an entire experience for your audience through a video or audio file.

So, for a small investment, you make in speech to text software, you would be able to add life to your content. In this blog below, we will be going through everything you want to know about audio transcription briefly, while also discussing the experience of being an audio transcriptionist.

What is Audio Transcription?

One of the most common queries that I receive, whenever I advertise for the need of an audio transcriptionist, is the candidates asking “what is audio transcription?”

Well, to put it simply, audio transcription is the speech to text conversion. Yes, you can relate it to your good old childhood days when your teacher dictates you to improve your writing skills. Obviously, it’s no more limited to the scope of the teacher’s dictation, but I am sure it gives you a fair idea about what we are talking about.

Today, audio transcription is used in virtually every industry from public speeches to business meetings and courtroom proceedings. And if we move to the most recent definition of the work, it is about converting the digital recordings into text. This is to say that as an audio transcriptionist, you may be given digital proceedings of a webinar or an interview conducted over the phone recording app; to convert it into text.  

So, when you say “what is audio transcription?” it could be defined in several ways, however, at its core, it’s the written version of any verbal speech.

What is an Audio Hour in Transcription?

What is an audio hour in transcription is another frequent query asked by many aspiring transcriptionists. An audio hour in transcription refers to the time required to convert one hour of audio into written text. The standard time to convert one hour of clear audio into transcription is 4 hours (4:1) – or we can say that an hour of transcription is required for 15 minutes clear audio recording.  

Now, the above mentioned time is just standard and that the quality of the audio (clarity, speed), as well as, the skills of the transcriptionist will have the final say about the time required to transcript one hour of audio. In my experience, if the audio is clear with low noise, a slow speaker and an experienced transcriptionist may lower the transcription time required by as much as 3:1 or even 2:1.

Contrarily, if the audio quality is muffled or garbled, then the transcription time even with an experienced transcriptionist may sore to become 6 or even 10 hours of transcription time for one hour of audio.

So, when we talk about an audio hour in transcription, it simply represents the time required to transcript one hour of audio; which may take anywhere from two hours to 10 hours or more!

What is Converse Smartly Audio Transcription? 

Converse Smartly® is one of the most powerful and competent speeches to text software that enables businesses to transcribe any length of audio fast and precisely. Developed by Google speech to text service, the software is meant to assist businesses and individuals alike to improve their efficiency and accuracy of audio transcription requirements. The advanced features of the platform can also be used to analyze the speech with multiple voices (team meetings, interviews, or webinars).

Converse Smartly® is powered by robust cutting-edge technologies to deliver the most precise results possible in the speech to text industry today. To enable higher productivity and efficiency for businesses, the software also includes various built-in tools enabling users to leverage the full potential for the platform.

Powered by cognitive learning, the Converse Smartly® is developed using deep-learning neural network algorithms that offer unprecedented accuracy for speech to text conversion. The machine-learning algorithms powering the platform also enable the software to consistently improve the accuracy by learning the voices and words over time. 

Key Features of Converse Smartly® 

Apart from its unprecedented accuracy and speed for speech to text conversion, some of the advanced features of Converse Smartly® includes:

Speech Analysis

The platform comes with the capability to analyze speech patterns. The dedicated speech analysis capability of the software is meant to understand the speech content and recognize spoken words with more accuracy.

Text Analysis

The text analysis feature of the platform enables the advance representation of text as data which subsequently may be used to mined for insights and pattern analysis.

Multiple Speaker Detection

Another advanced feature of the Converse Smartly® is the multi-speaker detection which enables identification of speakers by partitioning the audio stream into smaller homogeneous segments.

Some of the other features of Converse Smartly® includes:

Automated summary generation

Real-time transcription for English language speeches

Sentiment analysis

Built on Google Speech and IBM Watson technologies

Generate word cloud

What is a Transcriber Definition? 

Just as there can be multiple definitions for transcription services, similarly there may be multiple definitions of the transcriber. In essence, the transcriber is the person/process that is used to convert audio into text. Going by this definition, a transcriber can be a human that has the understanding of the language and context; performing the speech to text conversion; whereas, the transcriber may also be an automated platform/software like Converse Smartly® that is used for voice recognition and speech to text conversion.

Advantages and Benefits of Audio Transcription?

Transcription services have various benefits and advantages depending on your goals and objections. For starter, businesses and individuals alike can leverage the accuracy and affordability of audio transcription services to add value to their audio/video content. Also, companies may leverage the benefits of audio transcription to record and generate text files for the webinars, group meetings, or client meetings.

Another benefit of audio transcription is that it enables businesses to reach a wider audience by creating text in different languages. Similarly, by creating an email marketing content out of a popular video is another beneficial use of the audio transcription services. Whereas, the service can also be used to generate an in-depth blog post through a popular podcast episode.

Folio3 is Your Best Audio Transcription Tech Partner

Thanks to the highly experienced and expert team of developers at Folio3, the company has established in the speech recognition technology with its powerful and advanced speech to text software; Converse Smartly®. The platform is already being used by some of the leading businesses across the world and has been recognized as a leader in Cognitive services by the “Cognitive Services – Global Market Outlook (2017 – 2026).

What is the best transcription software audio to text?

Honestly, the best transcription software for audio to text depends upon your individual needs and requirements. There are multiple great audios to text converter that can come handy for businesses and individuals to cater to their needs of audio to text transcriptions. Some of the best transcription software audio to text includes:

Google Speech

Converse Smartly®

Dragon Anywhere

What is it like to work in an audio transcription field?

As an audio transcriptionist, you would be listening to audio/video files of various length trying to hear the audio and convert it into text, while keeping up the context and integrity of the sentences and phrases. You may be working with single or multiple industries like legal, medical, and others; thereby, you would have to identify jargon and technical terminologies in each industry. As for the compensation, it will vary depending upon your skills and experience. Generally, you may be making from $20 to $45 per hour for the transcription services.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at [email protected]

Writing isn’t an easy task, especially when you got to type thousands and thousands of words each day, it becomes increasingly difficult to keep up with the efficiency and quality of the writing.

As a content writer, I understand the pains experienced when you are required to type thousands of words. You write a few hundred words and then feel the need for stretching of changing the pose or even lie down for some time to get over the fatigue.

However, when I am exhausted typing, I switched to talking using speech to text software. This talking your way through the document isn’t just easy but it's more efficient and productive, while you also got the freedom to stretch or lay down or stretch without interrupting your daily productivity. In fact, I personally believe google speech to text is the better alternative to typing and assist you to come up with more creative ideas.

How Do You Use Speech to Text in Google Docs?

Now that you have the basic idea of the technology and understand the pros and cons of the system, let’s move to our main topic and see how do you use speech to text in google docs, and how do you use text to speech in google docs.

How Do You Use Speech to Text in Google Docs Using Android?

Step 1: Create a Google Document

The first step to use the speech to text in Google docs using android comprises creating a new Google document, which means you would need a Google account. Simply click on the “+” sign located at the bottom right corner of the application to create a new document. 

Once the document is created, you can start typing the document, however, with a small screen (compared to desktop or laptops) it isn’t the easiest task to type a long document over android devices. Thereby, you could switch to “Voice Typing”. You will find the option of “Voice Typing” on your Gboard.

Step 2: Voice Typing

Voice typing is just an easier, better, and faster alternative to writing using an android device. It’s a common practice and you must have seen many people talking to their phones for that sake. So, once you are in the voice typing mode, you can simply start dictating the software and let it do the rest for you.

A tip, in this case, is to use a headset for the purpose and be at a silent location to ensure the quality of the text.

Step 3: Enrich the text

Even better it’s not just the text that you can create using speech to text software, rather you can greatly enhance the richness of the content by inserting images, tables, and other elements. There are various formatting tools available to help you enrich the final product.

How to Enable Text to Speech Features with Google Docs on Chrome OS?

The speech recognition technology is also available for Chromebooks, where you can leverage the speech to text software as an alternative to typing using google docs. The technology also enables you to perform web searches through voice commands directly into your browser. The prerequisite here is to have a valid internet connection because your queries or commands would have to be sent to Google’s online servers for the conversion.

How Do You Use the Text to Speech in Google Docs on Chrome OS?

To be able to perform the text to speech conversion in Google docs on Chrome OS, follow the below-mentioned steps:

Web browsing through voice commands

Open your web browser and browse www.google.com 

Once you are on Google landing page, tap the “Microphone” icon adjacent to the tap box

As you tap the “Microphone” icon, you will see “Speak Now” prompt

Start to speak; you words will appear on-screen

Once you are done speaking, the command will automatically be used as a search query

Speech to text conversion

Now, if you are looking to use speech to text option in Google docs, online forms or elsewhere, you will have to tap the “Enable dictation” feature by below mentioned steps:

Open the “accessibility settings” on the Chromebooks

Choose Setting>Advanced >Manage accessibility features

Once you are in the accessibility feature, click the “keyboard and text input” section

Here, click the “enable dictation” feature (speak to type)

You will see a microphone icon located at the bottom right corner

You can click the microphone icon whenever you want to speak to enter text

Once you stop speaking, the dictation would automatically be over

Voice typing using the on-screen keyboard

If you wish to use the on-screen keyboard on your Chromebook for dictation, you can simply click on the microphone icon found on the keyboard. In this case, you won’t need to perform the above-mentioned steps to enable the option as it is enabled automatically.

How can I use speech to text in google docs using web add-on?

A third-party Add-on for Google Docs on the web also enables Speech Recognition (Figure C). Unlike the on-screen keyboard, the Add-on works only within a Google Doc; the Chrome OS on-screen keyboard works with all text fields.

To install the Speech Recognition Add-on, open a Google Doc, choose Add-ons, and then select Get add-ons. Next, search for Speech, then choose the + Free button to add it. Every time you want to start voice recognition, go to the Add-ons menu, choose Speech Recognition, and click Start. A sidebar will appear to the right of your document. Choose your language and dialect, select the blue Start button, and then start talking.

How do you use text to speech in google docs on apple and windows?

Apart from Android devices and Chromebooks, speech recognition technology is also offered by Windows and iOS devices. For iPhones or other Apple mobile devices, the speech recognition technology works much similar to the Google Voice Typing (as described for android devices). You can simply tap the microphone icon on the keyboard and start to speak. Alternatively, on Mac OS, you would need to enable the dictation much similar to the Chromebooks.

As for the Microsoft devices, you will need to change the system settings to enable the feature, while you would also have to spend some time to train the system for voice recognition before it’s ready to use.

Folio3 is your best tech partner for google speech to text

Folio3 is a leading technology partner offering businesses innovative and technologically backed solutions for cross-sector applications. The Converse Smartly® is a trademark speech to text software from Folio3 that comes with robust features and high accuracy to enable individuals and businesses alike, improve their efficiency, productivity, and quality. Converse Smartly® is ideal for organizations looking for an accurate and fast software to analyze speech or dialogues during seminars, group meetings, and interviews.

What is Speech to Text Software?

While I believe that most of you would be relatively familiar with the speech to text software, still for those who aren’t familiar, the speech to text software is a transcription technology that’s able to automatically transform spoken words into text. Using the software, you are able to transcribe, write speeches, blogs, articles, or even books by simply speaking to the software.

Don’t confuse the speech to text software technology with the virtual assistant apps, which are primarily meant to understand and respond to voice queries and complete the commands. In this case, technology is primarily meant to understand the spoken words and convert them into text.

In fact, these are few transcription software available that also comes with the assistive technology, giving you the best of both the worlds.

Today, the speech to text software is available for Windows, iOS, Android, and Mac operating systems, thereby, you don’t have to worry about the compatibility issues.

Here in this blog, we have come up with details of how do you use speech to text in google docs or how do you use text to speech in google docs. However, before we look into the how-to details; first let’s look at the technology, its benefits, and how to choose the best software.

What to look for in a Speech to Text software?

Before you invest in any speech to text software, it’s important to complete your background research and choose the best software that meets your needs and requirements.

How precise is the speech to text software?

Honestly, the technology has advanced greatly in the past decade and today the accuracy of the software would surprise most of the users using the technology for the first time. 

Having said that, not all speech to text software are developed equally efficiency and some of the software offered in the market do have inaccuracy and buggy issues, which makes them ineffective for professional use.

Thereby, when you are going for a speech to text software, the first consideration should be the accuracy and preciseness of the software; which most of the time will depend on the maturity of the software to be able to detect and identify the spoken words.

Compatibility

The next important aspect to consider is the compatibility of the software with multiple devices. As said before, the voice recognition technology has advanced greatly and today there is various highly accurate software that comes with full compatibility with the different devices and platforms. 

Remember when we said that there’s a difference between speech to text technology and the technology used in virtual assistant apps?

Fortunately, today many speech to text software includes the virtual assistant technology as well, which means that now you don’t only get to rejoice the freedom to get your spoken words typed automatically, but the software is also able to send you reminders or browse information for you.

So, basically, if you are looking for software that makes your world a lot easier, the speech to text software is the ideal investment you should make.

If Speech to Text Technology User-friendly?

If you choose a credible and reliable software, you can be sure of its user-friendliness, as well as, its various benefits for your daily work routine. 

Moreover, many reliable developer firms of the technology offer technical support for users to get them started with the software; either through online resources or even with dedicated training and customer support.

Budgetary Considerations

Last, in line, we have the budgetary considerations for choosing the speech to text software. Honestly, the technology doesn’t come cheap, and thereby, you shouldn’t be looking for the cheapest way out. That’s because going with the cheapest software may translate into the wrong choice of software with inaccuracy and buggy issues; leaving you frustrated for loss of money and time. Thereby, the best way to choose software is to compare the pricing of different software and choose the one that meets your technical requirements and budget.

Pros & Cons of Speech to text Software

Honestly, the speech to text technology has been a game-changer for writers, transcription service agents, and others who are required to type thousands of words on a daily basis. The technology not only improved the efficiency and productivity of writers and other professionals, but it also improved the quality of the work. However, like any other technology, the speech to text software comes with its own sets of limitations along with various benefits.

Let’s have a brief look at some of the pros and cons of the system:

Pros:

Fasten the writing/typing process and saves much time

A better alternative to writing in a conversational style, ideally suited for blogs, speeches, and other writing assignments

Enable you to multitask – keep doing your routine chores (cook, clean, etc.) while dictating to the software

Improve efficiency and productivity

Great technology for people who aren’t able to use their hands

Cons:

Users may need some time to get over the process and understand all the customization options

Few unreliable software may lead to inaccurate speech to text conversion

Some of the immature and unreliable software may have difficulty in identifying foreign words

The quality of the text may get compromised in case there is a background noise

You will have to edit and format the document

The resulting tone of the document compiled with speech to text software may be different from your typical writing style, which means that you may need some time to come up with a balanced new tone.

How do you use the chat in google docs online?

To use the chat option in Google docs online, open the Google docs. The number of participants collaborating on a Google doc doesn’t matter and that the system allows all the participants of a doc to send and receive the chat messages at once, as open group communication, which makes it a highly powerful feature.

To use the chat option, you would have to click on the name of the participant/s to start a new chat window. You can continue to chat with the participant/s using this window without having to leave the document. Thereby, it’s not just a great time saver feature but also reduces your dependence on third-party chat apps or messengers.

How do you download voice typing on google docs cloud?

You will need the latest version of Chrome and a microphone to download the voice typing on google docs cloud. Just follow the below-mentioned steps:

Visit docs.google.com and sign in to your Gmail account

Open a new Google doc in chrome

Go to tools>voice typing

Choose from 40+ languages and accents from the language option

Click the “microphone” icon and click “Allow” to use your microphone

Start to speak.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at [email protected]

White, sandy beaches, and big ocean waves- just what you need on your beauty vacation. But, beware of the danger lurking underneath with giant mouths and sharp teeth that can rip you open- yes, you are right; we are talking about sharks. You should not be scared of them, but instead, go ahead and plan your vacation, as sharks can be detected easily, thanks to the drone shark. Folio3 provides machine learning solutions that help work to be carried out faster and with greater accuracy. The AI-driven software for drones allows in ariel data collection and analysis. These drones can even perform tasks independently, without any human intervention, but with the use of Artificial Intelligence (AI) and data and analysis. Shark drones perform visual data analysis and image-based functionalities.

How To Use The Drone And Spot The Shark Easily?

Drone shark is a cost-effective and practical method to monitor the vast beach coast. With the help of AI, computing power, and drone technology, sharks near swimmers can be identified, and lifesavers can be alerted to take all the necessary precautions to prevent any shark attacks fro happening. The drone shark is an important device for countries with beaches and coastal bays. The shark drones have a camera attached to it, which helps to capture real-time aerial images. The system analyzes the video and identifies all the objects detected and puts them into one of the sixteen types of categories. These categories are:

—Sharks

—Whales

—Dolphins

—Rays 

—Different types of boats 

—Surfers 

—Swimmers 

If the drone shark detects a shark, the operator is notified through a visual message on the computer screen and also with an audible alert. Then, the operator verifies the alert and notifies the lifesavers promptly, and action is taken accordingly.

What Are The Benefits of Drone Shark?

The fat shark drone has provided many benefits. These benefits include:

- The shark drone can monitor a vast area in lesser time than humans.

- The drone shark is capable of providing an accurate, ariel view of the beach, which is not quite possible for humans to do. Humans can use helicopters, but they are costly to use.

- The shark drone can process the real-time images within the blink of an eye. With the use of AI, the drone shark can process real-time images within the blink on an eye and notify the operators promptly. This is much quicker than what the human eye can do.

- The fat shark drone helps in preventing shark attacks. As the shark drone is very quick to process images, and it also monitors a vast area of the beach in less time, the number of shark attacks can be reduced significantly.

- With the use of shark drones, the number of lifeguards hired, just for patrolling, can be decreased.  

Since the drone shark happens to be much more quickly than humans, the number of people hired just to monitor the coast can be reduced. This saves up on some money which the authorities can use to purchase more shark drones. However, the drones cannot replace humans altogether, but they can surely assist them.

Best Shark Drones In 2020

The top five shark drone companies are:

—SharkSpotter 

—Little Ripper Group

—JTT Technology's JTT T60

—Shark Shield's Phoenix Aerial's Vapour 55 UAV

—DARTdrones

There are numerous other companies too, but these five are considered to be the reliable and best ones so far.

Folio3 Is Your Best Drone Shark Tech Partner 

Folio3 combines the power of AI and computer vision technology to help these drones in instantly capturing and efficiently analyzing the real-time images. These drones can be used for different purposes, such as livestock management, terrain mapping, precision agriculture, and many other industries. The Folio3 drones can also be used in detecting sharks on the coast and can prove to be your best drone shark tech partner. With the AI Automation Solution based on open-source technologies offered by Folio3, they can provide you with their drones, which can assist different procedures of your company to get accurate and quick results. Similarly, these drones can be programmed to work as drone shark and help in identification of sharks on the shore, for the security of surfers and swimmers along the coast.

FAQs:

Why are the sharks swimming close to the shore?

During the summer months, it is common for the sharks to swim closer to the shore. They feed on fish and seals closer to the shore. Also, the number of surfers on the coast increases during the summer months, which causes the sharks to investigate the surfers, with chances of preying on them being prevalent. Some sharks generally like to swim in shallow waters. Such as bull sharks and blacktip sharks. Tiger sharks tend to be very curious and aggressive, with an excellent sense of smell. The Shark Alley beach in Gansbaai, South Africa, is the world's most shark-infested beach.

Does the drone really capture sharks near the surface?

Yes, the drone has the capability of capturing sharks near the surface of the shore. With the help of AI, the fat shark drones can process the real-time images within a fraction of a second, and classify the moving object seen according to different categories and then sends notifications to the drone operator accordingly.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at [email protected]

We are living in interesting times, and there’s no doubt about it. I mean who in their right mind have thought that humans and machines would coexist in a way that humans won’t even be able to tell how machines are making their lives easier? Today, machines are outperforming humans in every aspect. They have altered our ways of interacting with other humans, as well as, other machines as well.

Now, that being said, we are in no way under robot invasion right out of a sci-fi movie. Rather the machines that surround us are created and programmed by humans. Yes, today we will be talking about the machine learning engineers; the humans who program and bring life to the machines to perform their own. A machine learning engineer is responsible to program the machines, so it is able to experience, work, and even feel like us.

Now, with so much potential, machine learning engineers are one of the most in-demand professionals in the IT field. According to one report from Jobs and AI Anxiety, which looks at the future of work, and the influence of technology in the job market, the machine learning engineers will be the most sought after professionals. This is in line with the other findings of the report that details that over 30% of the managers reported the use of Artificial Intelligence (AI) and Machine Learning (ML) in their companies, whereas, the other 53% responded that they are looking to incorporate the technology in near future.

Looking at the stats, it is safe to claim that the future belongs to the machine learning engineers and as more businesses and industries explore the potential returns of investing in these disruptive technologies including ML and AI, they can rest assured for a bright and highly in-demand job market.

With such a bright potential, we know many youngsters would be looking to get more insights into the profession and how much do machine engineers make. So, in this blog we have come up with all the relevant details that will help fresh machine learning engineers, as well as, aspiring youngsters to know all about the potential of the profession and how much do machine learning engineers make.

So, let’s start exploring the potential of the profession.

What to Expect in a Machine Learning Engineer Job Description

The machine learning engineer salary will depend directly on his/her job description and roles for which he/she is hired. Now, since it’s still early days for machine learning and there aren’t machine learning engineers available in the industry, thereby, the job description of machine learning engineers will typically resemble that of data scientists, except that there will be a strong emphasis on programming skills.

Contrarily, the job description may also look out for professional programmers and coders with good skills in data management and analysis. Again, the machine learning engineer salary will depend on the job description and the role (junior, senior) he will be hired for.

Here is some prerequisite job description for machine learning engineer:

-  Advanced degree (Graduate, Masters) in computer science, mathematics or statistics

- Skills in data modeling and architecture

- Proven programming skills in multiple languages preferably in Python, R or Java

- Basic understanding and experience working with different machine learning frameworks like Keras or TensorFlow

- Advance mathematics skills

- Good communication skills

Machine Learning Engineer Salary in 2020

Since it’s still a fairly young job title with not enough data point to accurately give the range for machine learning engineer salary, we need to make some predictions based out on the salaries from data scientists and software engineers. It’s the right approach to predict how much do machine engineers make because machine learning engineer job description falls within the scope of these two fields. So, here’s a basic structure for machine learning engineer salary in 2020 against various positions:

Big data engineer: The salary would average at $163,250

Data architect: The salary would average at $141,250

Data scientist: The salary would average at $125,250

Data modeler: The salary would average at $101,750

Developer/analyst: The salary would average at $108,500

Software engineer: The salary would average at $125,750

What Does a Machine Learning Engineer Do?

If we are to speak broadly, the job of machine learning engineers is quite similar to some of the other roles in the data science industry like AI engineers, and data scientists. All of these positions involve working with the massive amount of data, needs a strong and polished data management skills, as well as, the ability to develop complex models to solve the problems using the massive data available to them.

However, with these three generic roles, the similarities come to end between machine learning engineers and data scientists. That’s because while data scientists will be developing critical and high-value insights from the data in form of reports or charts with human readability, machine learning engineers will be working to develop complex algorithms and models to enable machines to take over the predictive modeling. They will be using the massive data at their disposal to train the algorithms for specific tasks (that’s where the machine learning stems from), and once the model is trained it will be able to perform the tasks itself without the need for human intervention.

If you haven’t already versed with our routine interaction with a machine learning algorithm, the “recommendation algorithm” used in almost all e-commerce stores including Amazon, eBay, Netflix, and others is the simplest and brightest example of how machine learning algorithms are able to predict our preferences based on our profile and previous interaction with it. Every time you buy or even search a product on these sites, you add data to your profiles, which is then analyzed and processed by the “recommendation algorithm” to give you the most suited recommendations for more products or films you may like to watch. Interestingly, just like humans, the machine learning algorithms don’t stop learning and with each interaction, it continues to improve its accuracy and content value.

Machine learning is quite closely associated with other disruptive data technologies like Artificial Intelligence and Deep Learning. It actually can be said as a subfield of Artificial Intelligence, which is aimed to create intelligent and learning machines. On the other hand, deep learning is also a subfield of artificial intelligence, which is concerned with creating Artificial Neural Networks (ANNs), which are able to understand, analyze and solve multi-layers data sets. Virtual assistants, Chatbots, and driverless cars are some of the practical examples of deep learning.

How to Sharpen Your Saw in Machine Learning Engineer World (Update your Resume)

If we are to analyze the resumes of machine learning engineering aspirants, the most common weak part is the programming experience. This means that if you are seeking a job title of machine learning engineer, you got to have extensive programming experience in multiple languages. Python is especially the most important and most widely used programming language for developing machine learning algorithms, which is why you should get hands-on experience with Python programming. And since it is almost the default programming language for machine learning algorithms (not the default), there are various robust and intensive Python tutorials available online that can help you get experienced with Python language. The other important language for machine learning in R, which is also widely used in the industry. Both Python and R are also relatively easy languages, so you won’t have much difficulty in learning those languages. Other relevant languages for machine learning include Java and/or C++.

Apart from programming skills, you would also like to develop strong skills in dealing with massive data sets. One of the easiest ways to build your data management and analysis skills is to join Kaggle; a Google initiative to help data science aspirants learn the profession by working on data science projects. There are a variety of datasets available at Kaggle, shared by data scientists and machine learning experts from around the world; making it truly a great resource to polish up machine learning skills.

Also, you may wish to take up machine learning courses offered by Amazon – the tech giant and creator of Alex (virtual assistant). Amazon offers a high-value machine learning and certification course which is offered with four pathways including data platform engineer, business decision-maker, and developer and data scientists. Not only these are really great and enriched resources to learn machine learning, but the best part is that Amazon is offered all these courses for free.

Machine Learning Salary for Fresher or Drop Out

When we talk about machine learning fresher, it basically employees with a machine learning engineer with 0 to 4 years of experience. This would typically be a college graduate who has worked around at different companies in data science or machine learning role.

The machine learning engineer salary for fresher will vary broadly depending upon the role, job description, location, and the company. However, the average salary for a machine learning engineer is $97,000 approximately. Couple this with the bonuses and profit-sharing offered by companies and this can rise quickly to as much as $130.000.

Here are some skills that can improve your paycheck as a machine learning engineer:

C++ Programming Language

Python

Image Processing

Big Data Analytics

Natural Language Processing

Computer Vision

Data Analysis

Deep Learning

Software Development

Folio3 is Your Best Machine Learning Tech Partner

At Folio3 we bring extensive experience and an enhanced understanding of machine learning needs of customers. We have helped hundreds of businesses develop successful Machine Learning as a service solution. We have successfully developed customized machine learning solutions from ATM cash forecasting to Clinical Decision Support System (CDSS), establishing our dominance and authority as a reliable and capable machine learning Service Company.

Why Machine Learning Engineers are in Demand

As we said above, machine learning is all about leveraging the data to create intelligent machines. And if we are to look at the business models today, it’s highly dependent on data management and analytics. From clients’ interaction to internal systems and processes, businesses need to manage and analyze data effectively to gain a competitive edge over competitors. And in this quest, machine learning is the aptest technology that can help them leverage the full potential of the company’s data.

Here are some of the applications of Machine Learning in the practical business world:

Image and Speech Recognition

Machine learning assists businesses to implement automated systems and procedures to convert unstructured data into useful information like auto-tagging of images, speech to text and text to speech converter, and many other tasks.

Customers’ Insight

as we discussed above, the machine learning algorithms are able to learn the preferences of customers through their previous interactions with the business, thereby, giving them valid, highly-relevant, and targeted recommendations for personalized services.

Risk management and Fraud Prevention

The ability of machine learning algorithms to analyze and process a massive amount of historic data make them perfectly apt to analyze historical records of banks and other financial institutions to make accurate financial predictions, as well as, spot any fraudulent activity in real-time.

How much do machine learning engineers make in California?

The machine learning engineer salary greatly varies depending upon the location of the company. New York and California remain two of the top cities offered the highest salaries for machine learning engineers.

According to statistics, machine learning engineers are offered a 24.4% higher salary in California, as compared to the National average.

How much do Facebook machine learning engineers make?

Facebook is a tech giant and a major player when it comes to the implementation of machine learning algorithms. The social media giant hires hundreds of machine learning engineers with good payout. On average, a machine learning engineer at Facebook takes out $123,395 salary. The salaries can range from $15,000 to $188,000.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at [email protected]

Deep learning and machine learning is big today. People are looking out for ways to get their hands with this futuristic technology. However, one common question that is frequently asked by newbies and learners is how many images are required for deep learning classification?

Honestly, I hate to say but there is no one clear answer to this question.

I mean, back in days I remember when Google Brain was in initial phases, the tech giant used over 15,000 images to train the algorithms, however, today this may be done with few hundred images or even less.

The right answer to this question is that it will depend on many factors including the complexity of the problem and your algorithm.

So, while I can’t answer the question of “how much images you may need for deep learning classification”, I can certainly give you some of the ways to think the deal with your question.

In this blog, I have come up with some steps that can help you to come up with the right number of images you may need for deep learning classification.

So, let’s dive into it.

What’s the Reason Behind this Question? 

You may not realize but to be able to get an answer for the size of the training dataset you may need, it is important to know why you want to know this, as your answer may influence the next steps.

For example:

You may have large data.

If this is your answer than you may consider developing some learning curves or may decide to go with a large data framework to be able to use the maximum amount of data.

You may have very scarce data

If that’s your reason for the question, you may want to consider some other options to collect data, or may opt for data augmentation methods, which can help you to artificially enlarge the size of the training data

You haven’t started collecting data, in this case, you may want to collect some data initially and see if that’s enough for the algorithms. In case of data collection is expensive for you, talk to a domain expert for a specific answer.

Ok, now let’s see how many images you may need for deep learning classifications.

1) It Depends

As I said earlier, there is no one size fit all model in this regard, so I doubt there would be anyone who can give a generic answer without understanding the specific predictive modeling problem.

The answer to this question would have to be found by you only through empirical investigation. Some of the factors that may influence the amount of data required for training purpose include:

The complexity of the problem; and

The complexity of the algorithm

And this will be your starting point.

2) Reason by Analogy

Thousands and thousands of programmers and data scientists have worked on the deep learning models before you and many of them have published their studies, which in most cases are available for reading free.

So, before you may ask anyone else the size of the dataset required for training purposes, it is better to go through the similar studies that have been done previously to get a better estimate for the amount of training data you may require for the classification.

Also, many studies have been done to estimate the optimum performance scale for algorithms with respect to the size of the training data. Such studies can greatly help you to predict the right amount of data required for specific algorithms.

In fact, you may want to average the findings of multiple studies to get a good estimation.

3) Use Domain Expertise

For training the algorithm, you would need to chunk out a sample data representative of the problem, which you are looking to solve.

Now, it is important to remember that you want the algorithm to learn the function to be able to map input data to output data. Now, the learning performance of the algorithm for the mapping function will depend on the quality and quantity of the learning data you input in the model.

This also means that to train your model to higher performance levels, you would need to have large training data to help the model to understand all different relationships that may exist in the data and to be able to learn them and map.

For this reason, you may want to consult the domain expert (for the problem you are trying to solve) to understand all possible functionalities, features, and relationships that would be required to be learned by the model and to completely understand the complexity of the problem.

4) Statistical Heuristic

If you haven’t heard of the Statistical Heuristic methods, then these are the models that can be used to estimate the appropriate training data size.

The best part about the Statistical Heuristic methods is that the majority of these are designed for the classification problems, so it may come handy in your case as well. While some of the Statistical Heuristic is comprehensive and robust, others may at best be defined as ad hoc.

5) Dataset Size vs Model Proficiency

It’s not uncommon in data science to demonstrate the performance scale of algorithms against the size of the training data, or the complexity of the problem.

Unfortunately, not all of these studies are published and available for review purposes, or those that are published may not relate well with the type of problem you are looking to solve.

For that reason, I often suggest aspiring data scientists continue with the available data with all the data that’s available to you and any available learning algorithm.

Basically, you would be performing your own study to determine the performance scale of the model against the size of the training data.

The results of the study may be plotted with the model skills on the y-axis and size of the training data on the x-axis. This will give you a fair estimate of how the size of the data will affect the model skills in your specific problem.

The plotted graph will be called a “learning curve”.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at [email protected]

Machine learning (ML) is a subfield of the Artificial Intelligence (AI), which give machines the ability to learn and adopt from their experienced and enhance their ability to complete any specific tasks assigned to them. The most prevailing example of machine learning is the “people who bought XXX also saw XXXX”, which you must have seen across all ecommerce stores.

What is Machine Learning?

The term machine learning was coined by Arthur Samuel, in the year 1959. Arthur Samuel is also considered as the pioneer of Artificial Intelligence. According to him, machine learning is a field of study that enables computers to adapt and learn for themselves without any explicit need for programming. Obviously, this was the beginning of what we are seeing today, but machine learning as we know it today is pretty much what Arthur defined way back in the 1950s.

In this blog, we will be covering all aspects of machine learning including the working of machine learning, and machine learning process steps. We will also be looking at how does machine learning to work in today’s world, as well as, define some of the popular machine learning techniques used widely in different industries. last but not the least, we will also be looking at the best programming languages for machine learning, while finally rounding up our blog by summarizing the working of machine learning.

As said earlier, machine learning is a subfield of artificial intelligence. In the most basic terms, the machine learning algorithms are meant to create intelligent programs that are able to get trained for specific tasks by themselves and learn better ways to complete the tasks faster and with precision. It’s kind of similar like creating algorithms that replicate the human mind, with the ability to learn, adapt, and make intelligent decisions. Such algorithms are developed to minimize the need for human intervention. For the most part, the learning process of the machine is completed through supervised or unsupervised training with a large volume of the training dataset. The quality and volume of the data used to train machines are directly related to the preciseness of the machine learning models. Machine learning models can be developed for explicit tasks, where automation is desired.

How does Machine Learning work in today`s world?

Model, parameters, and the learners are three fundamental blocks of any machine learning model.

The model represents the system that is responsible to make forecasts/predictions. 

Parameters are the characteristics which are considered by the model to make forecasts/predictions.

The learner component of a machine learning model enables models to make learn and make necessary adjustments required to make accurate forecasts/predictions.

Now, to help you better understand how does machine learning work, we can consider an example. In this scenario, let’s consider that you have two different drinks; beer and wine. Now, you want machine learning algorithms to differentiate between the two drinks based on some fixed parameters which in this case would be the color and percentage of the alcohol found in each drink.

Here are the machine learning process steps that will complete the given task for you:

Learning from the training set

The first and foremost step for any machine learning model is to feed the model with a structured and large volume of data for training. In this case, you would require several (possible hundreds) samples of beer and wine with defined color and alcohol percentage. Now, you will feed the training data into the model and classify each of the samples as per their defined parameters. For instance, you would define the percentage of alcohol in samples of wine against the percentage of alcohol in samples of beers. Similarly, you will classify the other defined parameter that is ‘color’ for samples of wine and beet.

For easy representation, you may define the ‘color’ as parameter ‘X’ and alcohol percentage as parameter ‘Y’. Now in this case the (X, Y) will be the defined parameters of the training dataset for the model and will help the algorithm to adapt and learn differentiation between each of the drinks.

Measuring Error

The next logical step in our “how does machine learning work” is to measure the errors and discrepancies in the results of the model. For this step, you will input a fresh dataset (different from the training data) and the outcome of this step could be either one of these four:

- True Positive: this is the most ideal scenario where the machine learning model is able to predict each type of drink correctly

True Negative: in this scenario, the model misses out on classification of drinks when it is present.

False Positive: in this scenario, the model wrongly classify drinks when it isn’t present.

False Negative: in this scenario, the machine learning model isn’t able to classify drinks when it is present.

To estimate the total error from the model, you will use the sum of FP and FN

Manage Noise

Now, while to help you understand “how does the machine learning work” better, we have kept the set of defined parameters limited to only two. However, in real-world scenarios, there may be hundreds and thousands of parameters that have to be defined in the training data to enable machine learning models to classify the items precisely.

In such scenarios, there will be a large number of errors estimated in the second step. These errors will be sourced from the noise present in the training data. Noise in this case represents the unwanted anomalies that deviate the standards of the defined parameters; thus weakening the learning process of the model. The noise in training data may be present for various reasons including:

Large volume of the training data.

Unstructured training data.

Data labeling errors.

Overlooked attributes that influence the classification.

Testing and Generalization

In many cases, the machine learning algorithm fits perfectly with training data, however, it fails to produce results when a fresh dataset is an input to the model (other than the training data). This is why it is important to evaluate the fitness of the algorithm to the new/fresh dataset. This is achieved by giving new/fresh data as input and analyzing the results produced by the algorithm. The generalization in this case refers to the fitness of the model to make predictions for a fresh dataset.

How Supervised Machine Learning Works?

Supervised machine learning is one of the popular machine learning techniques. In this case, the model takes training data with known responses to the output to learn and build its capacity to make predictions for a new/fresh dataset. Supervised machine learning techniques should be used where users have a high-volume of structured data for training the model. In this case, the higher the volume and quality of the data, the better and more precise results can be expected from machine learning models. The supervised learning model makes prediction models based on regression and classification techniques.

Classification techniques 

The classification technique used by supervised machine learning models delivers discrete responses. For example, the model will simply inform if an email is a spam or genuine (you experience it in your email inbox). In classification techniques, the input data is classified into the defined categories. This technique is widely used in medical imaging, image processes, and speech recognition.

You can use classification technique if you have a structured, tagged, or categorized data which is divided into discrete classes or groups. For instance, the technique is used in handwriting recognition, where it is capable of classifying the handwriting based on the recognition of letters and numbers style. Unsupervised pattern recognition is widely used in image processes and computer vision to identify objects and image segmentation.

Some of the widely used supervised learning algorithms in the industry include Neural networks, support vector machine (SVM), K-nearest neighbor, logistical regression, and more.

Regression techniques 

The regression techniques forecast continuous responses. An example of these includes predicting the temperature changes or fluctuations in power demand. The regression techniques are used in algorithmic trading, energy load forecasting among others.

The regression techniques for classification can be used when the input data is in the form of a continuous range, or real numbers. Some of the regression algorithms include stepwise regression, linear regression models, non-linear regression models, adaptive neuro-fuzzy learning, and others.

How Unsupervised Machine Learning Works?

Unsupervised machine learning is used to identify hidden patterns and structures in data to draw inferences.

Clustering 

Clustering is one of the most widely used unsupervised learning techniques, which is used for exploratory data analysis to identify hidden patterns and structures in data. The technique is used in different applications including gene sequence analytics, object recognition, image processing, and others.

An example case study for the clustering technique is identifying the optimum location of building a cell phone tower for a telecommunication company. In this case, the unsupervised machine learning algorithm can be used to identify clusters of users in different areas who rely on cell phone towers. Since a cell phone may only be connected to a single tower at a time, the clustering algorithm can process the dataset and come up with the most suitable cell tower placement design to optimized signal reception for users.

Some of the common algorithms used to perform clustering include; fuzzy c-means clustering, K-means, Gaussian mixture model, and others.

Which Programming Language is best for Machine Learning?

Now that we are over the machine learning process steps and working of machine learning, let’s see which the best programming language for machine learning is.

This is rather a simple question, as Python is undoubted the most suited programming language that is also most widely used to develop machine learning applications. We will come to the advantages and strengths of Python as the best programming language for machine learning algorithms a little later. Some of the other popular languages that are used to develop machine learning applications include R, C++, Shell, Java, and others.

What makes Python the ideal choice for machine learning applications development is the simplicity and readability of the language, as compared to the other languages. This is important because developing machine learning algorithms is already inherently difficult with various complexities concepts like calculus and others involved in the process, which required a lot of time and effort to develop. The simplistic Python language is able to share some burden of the developers and ML engineers. There are various Python tutorials freely available online that can be checked to understand the basics of the language. Another great advantage of Python programming language for use in machine learning algorithms is its various pre-built libraries for machine learning. The language comes with various packages that can be used directly for various applications. Some of the Python packages widely used in machine learning applications development include:

Numpy, Scikit (used widely for image processing)

Numpy, Scikit, and NLTK (great when working with text)

Librosa (Audio applications)

Matplotlib, Scikit, and Seaborn (used for data representation)

TensorFlow, Pytorch (used for deep learning applications)

Pandas (used for high-level structured data analysis)

The language gives ML engineers and developers an option to choose between scripting or object-oriented programming. Moreover, the changes can be easily implemented without having to recompile the code.

Folio3 is Your Best Machine Learning Tech Partner

Folio3 is one of the leading machine learning application development companies in the world. With decades of experience in the development of high-tech and complex machine learning applications, Folio3 has partnered with some of the biggest companies from different industries and help them to initiate their digitalization process with robust customized solutions with advanced machine learning capabilities.

How Does Folio3 Machine Learning Solution Work?

Breast Cancer HER2 Subtype Identification

Folio3 partnered with one of the oldest public sector medical universities and hospital in Pakistan, Dow University of Health Sciences to develop a robust computer-aided assistance system with cell segmentation and spot counting capabilities to enable medical practitioners and doctors to perform faster and more precise breast cancer tests. The powerful computer-aided system was able to digitize and store the images for further analysis and processing.

Converse Smartly®- Speech to Text Software

Converse Smartly (CS) is a hugely successful in-house project of the company that was developed to establish the capabilities and expertise of Folio3 ML engineers in the fields of machine learning and Natural Language Processing.

Converse Smartly (CS) is an advance and robust speech to text application that uses state-of-the-art technologies including IBM Watson API, Amazon AWS, Microsoft Azure, Python’s Natural Language Toolkit, and Google Speech-to-Text API to deliver precise and outstanding results.

ATM Cash Forecasting

For this project, Folio3 partnered with one of the largest commercial banks in Pakistan, which is also a multination bank to develop an ATM cash forecasting system. The prediction model was able to forecast t4eh cash-flow management for over 2000 ATMs of the bank in Pakistan and globally. Based on the unique requirements of the bank, we developed a sophisticated and innovative predictive solution that increased the ATM management profits of the bank by up to 6%.

how does google assistant machine learning work?

Google Assistant is a personal assistant that leverage on the image recognition, NLP, and Google knowledge graph to converse with the users. It’s much like a personalized chatbot that using natural language processing to interact with the users and come up with the answers to users’ questions.

How does AI work with machine learning?

Machine learning is a subfield of a much broader Artificial Intelligence (AI) technology, which is meant to enable machines to execute tasks smartly. Machine Learning on its own is about developing intelligent algorithms for devices that can learn, adapt and execute tasks through their learned experiences.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at [email protected]