Speech-to-Text Accuracy Benchmark - October 2021

[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.


We have repeated the test using similar methodology as before: used 44 files from the Jason Kincaid data set and 20 files published by 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.

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Voicegain: Build Voice AI apps with our Speech-to-Text and LLM-powered NLU APIs. Record & Transcribe meetings, contact center calls, videos, etc. Get LLM-powered Summary, Sentiment and more. Build Conversational Voice Bots that integrate with your On-prem or cloud CCaaS platform. Get started today.

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