Today, we are really excited to announce the launch of Voicegain Transcribe. Transcribe is an AI based transcription assistant for recording and transcription of in-person and web meetings, live video events and webinars. Our goal is to empower users to focus on their meetings/events and leave the note taking to us.

Voicegain Transcribe is built on top of our highly accurate deep-learning-based ASR. It is powered by the same Speech-to-Text APIs that all our developer/platform customers use today. Our out-of-the-box accuracy of 89% is on par with the very best. 

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Currently there are 3 main ways you can use Voicegain Transcribe:

1. Transcribe Web Meeting (using browser sharing of audio)

Users can use our browser sharing feature to record & transcribe audio that is playing on any tab on a Chrome or Edge browser. Any meeting platform that allows a browser based client is supported. Some prominent meeting platforms include Google Meet, BlueJeans, Webex and Zoom.

If the Users use a Windows based laptop/desktop, then this Browser sharing supports capturing audio from the client desktop app of the Meeting platform (e.g Zoom or Microsoft Teams). The Mac OS users does not support sharing of audio from a desktop app with Voicegain Transcribe.

2. Microphone Capture

This allows users to record and transcribe anything that is captured by the microphone on their laptop/desktop. So Users can turn on the microphone capture for an in- person meeting, lecture or event. They can also just let a web meeting or event play on their speaker and have the microphone capture what is being played on it.

3. Upload Audio Recordings

Users may also upload pre-recorded audio files of their meetings, podcasts, calls and generate the transcript. We support over 40 different formats including mp3, mp4, wav, aac and ogg). Voicegain supports speaker diarization - so we can separate speakers even on a single channel audio recording.

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Languages Supported

Currently we support English and Spanish. More languages are in our roadmap - German, Portuguese, Hindi.

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Advanced Features

Voicegain Transcribe also supports the following advanced Features.‍

a. Projects

Users can organize their meeting recordings and audio files into different projects. A project is like a workspace or a folder.‍

b. Named Entities & Keywords

Users can highlight named entities (dates, currency, addresses, email id) in their meeting transcript.

c. PII Redaction

Users can also mask - in both text and audio - any personally identifiable information.

Coming Soon

We are adding close integration with the Zoom meeting platform. With this, we can capture the actual speaker labels directly from Zoom. This will address errors related to diarization.

We are also adding a Chrome extension that will make it much easier to record and transcribe web meetings.

Get Started for Free today!

By signing up today, you will be signed up on our forever Free Plan - which makes you eligible for 120 mins of Meeting Transcription free every month . Once you are  satisfied with our accuracy and our user experience, you can easily upgrade to Paid Plans.

If you have any questions, please email us at support.transcribe@voicegain.ai

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[UPDATE 1/23/22: After training on additional data, the Voicegain recognizer now achieves an average WER of 11.89% (an improvement of 0.35%) and a median WER of 10.82% (an improvement of 0.21%) on this benchmark.

Voicegain is now better than Google Enhanced on 44 files (previously 39).

Voicegain is now the most accurate recognizer on 12 of the files (previously 10).

We have additional data on which we will be training soon and will then provide a complete new set of results and comparison.]

It has been over 4 months since we published our last speech recognition accuracy benchmark. Back then the results were as follows (from most accurate to least): Amazon and Microsoft (close 2nd), then Google Enhanced and Voicegain (also close 4th) and then, far behind, IBM Watson and Google Standard.

Since then we have tweaked the architecture of our model and trained it on more  data. This resulted in a further increase in the accuracy of our model. As far as the other recognizers are concerned, Microsoft improved the accuracy of their model the most, while the accuracy of others stayed more or less the same.

Methodology

We have repeated the test using similar methodology as before: used 44 files from the Jason Kincaid data set and 20 files published by rev.ai and removed all files where the best recognizer could not achieve a Word Error Rate (WER) lower than 25%. Note: previously, we used 20% as the threshold, but this time we decided to keep more files with low accuracy to illustrate the differences on that type of files between recognizers.  

Only three files were so difficult that none of the recognizers could achieve 25% WER. The two removed files were both radio phone interviews with bad quality of the recording.

Voicegain now better than Google Enhanced

As you can see in the results chart above, Voicegain is now better than Google Enhanced, both on average and median WER. Looking at the individual files the results also show the Voicegain accuracy is in most of the case better than Google:

• Voicegain was better than Google Enhanced on 39 files
• Google Enhanced was better on 20 files
• They were tied on 2 files.
  

Other results

Key observations about other results:

• When you consider the average and median WER then Voicegain looks tied with Amazon having the median value better by 0.07% but the average value worse by 0.76%
• When you consider the average and median WER then Microsoft recognizer is better than Amazon with average better by 0.49% and median better by 0.69%
• When you look at the individual audio files the best scoring recognizers were:
• Amazon - was best on 29 files
• Microsoft - was best on 20 files
• Voicegain - was best on 10 files
• Google Enhanced - was best on 2 files

As you can see the field is very close and you get different results on different files (the average and median do not paint the whole picture). As always, we invite you to review our apps, sign-up and test our accuracy with your  data.

Out-of-the-box accuracy is not everything

When you have to select speech recognition/ASR software, there are other factors beyond out-of-the-box recognition accuracy. These factors are, for example:

• Ability to customize the Acoustic Model - Voicegain model may be trained on your audio data - we have demonstrated improvement in accuracy of 7-10%. In fact for one of our customers with adequate training data and good quality audio we were able achieve a WER of 0.5% (99.5% accuracy)
• Ease of integration - Many Speech-to-Text providers offer limited APIs especially for developers building applications that require interfacing with  telephony or on-premise contact center platforms.
• Price - Voicegain is 60%-75% less expensive compared to other Speech-to-Text/ASR software providers while offering almost comparable accuracy. This makes it affordable to transcribe and analyze speech in large volumes.
• Support for On-Premise/Edge Deployment - The cloud Speech-to-Text service providers offer limited support to deploy their speech-to-text software in client data-centers or on the private clouds of other providers. On the other hand, Voicegain can be installed on any Kubernetes cluster - whether managed by a large cloud provider or by the client.

Take Voicegain for a test drive!

1. Click here for instructions to access our live demo site.

2. If you are building a cool voice app and you are looking to test our APIs, click here to sign up for a developer account  and receive $50 in free credits

3. If you want to take Voicegain as your own AI Transcription Assistant to meetings, click here.