Build Voice AI apps with our Speech-to-Text APIs. Transcribe & analyze meetings, contact center calls, videos and more. All built on our highly accurate and affordable deep-learning ASR. Deploy in your infra or use our cloud. Train on your data to build custom models that you own and get high accuracy.
APIs for Developers • AI Assistant for Meetings • Automation & Analytics for Call Centers
Voicegain’s deep learning ASR offers an unbeatable combination of accuracy, price and flexibility. Voicegain ASR can be deployed on-premise, in your VPC or invoked as a cloud service. We integrate out-of-the-box with leading contact center, video meeting and bot platforms.
Our ASR is built on most recent advances in deep learning. We utilize end-to-end transformer-based deep neural networks and we have trained it with several tens of thousands of hours of diverse audio datasets.
APIs to embed transcription into your app and build voice bots accessible over telephony. Deploy Voicegain on your infrastructure (VPC, Datacenter) or use our cloud service
Get your own AI Meeting Assistant to automate note taking. Always know who said what when and where! Integrates with video meeting platforms like Zoom, Microsoft Teams and Google Meet. Edge (On-Prem or VPC) options available.
Automate Quality Assurance and extract CX insights from voice interactions in contact center. White-label or Source Code License of UI available.
It has been another 6 months since we published our last speech recognition accuracy benchmark. Back then, the results were as follows (from most accurate to the least): Microsoft, then Amazon closely followed by Voicegain, then new Google latest_long and Google Enhanced last.
While the order has remained the same as the last benchmark, three companies - Amazon, Voicegain and Microsoft showed significant improvement.
Since the last benchmark, at Voicegain we invested in more training - mainly lectures - conducted over zoom and in a live setting. Training on this type of data resulted in a further increase in the accuracy of our model. We are actually in the middle of a further round of training with a focus on call center conversations.
As far as the other recognizers are concerned:
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 none of the recognizers could achieve a Word Error Rate (WER) lower than 25%.
This time again only one file was that difficult. It was a bad quality phone interview (Byron Smith Interview 111416 - YouTube) with WER of 25.48%
We publish this since we want to ensure that any third party - any ASR Vendor, Developer or Analyst - to be able to reproduce these results.
You can see box-plots with the results above. The chart also reports the average and median Word Error Rate (WER)
Only 3 recognizers have improved in the last 6 months.
Detailed data from this benchmark indicates that Amazon is better than Voicegain on audio files with WER below the median and worse on audio files with accuracy above the median. Otherwise, AWS and Voicegain are very closely matched. However we have also run a client-specific benchmark where it was the other way around - Amazon as slightly better on audio files with WER above the median than Voicegain, but Voicegain was better on audio files with WER below the median. Net-net, it really depends on type of audio files, but overall, our results indicate that Voicegain is very close to AWS.
Let's look at the number of files on which each recognizer was the best one.
We now have done the same benchmark 5 times so we can draw charts showing how each of the recognizers has improved over the last 2 years and 3 months. (Note for Google the latest 2 results are from latest-long model, other Google results are from video enhanced.)
You can clearly see that Voicegain and Amazon started quite bit behind Google and Microsoft but have since caught up.
Google seems to have the longest development cycles with very little improvement since Sept. 2021 till about half a year ago. Microsoft, on the other hand, releases an improved recognizer every 6 months. Our improved releases are even more frequent than that.
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.
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:
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.
It has been over 7 months since we published our last speech recognition accuracy benchmark. Back then the results were as follows (from most accurate to least): Microsoft and Amazon (close 2nd), then Voicegain and Google Enhanced, and then, far behind, IBM Watson and Google Standard.
Since then we have obtained more training data and added additional features to our training process. This resulted in a further increase in the accuracy of our model.
As far as the other recognizers are concerned:
We have decided to no longer report on Google Standard and IBM Watson accuracy, which were always far behind in accuracy.
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 none of the recognizers could achieve a Word Error Rate (WER) lower than 25%.
This time only one file was that difficult. It was a bad quality phone interview (Byron Smith Interview 111416 - YouTube).
You can see boxplots with the results above. The chart also reports the average and median Word Error Rate (WER)
All of the recognizers have improved (Google Video Enhanced model stayed much the same but Google now has a new recognizer that is better).
Google latest-long, Voicegain, and Amazon are now very close together, while Microsoft is better by about 1 %.
Let's look at the number of files on which each recognizer was the best one.
Note, the numbers do not add to 63 because there were a few files where two recognizers had identical results (to 2 digits behind comma).
We now have done the same benchmark 4 times so we can draw charts showing how each of the recognizers has improved over the last 1 year and 9 months. (Note for Google the latest result is from latest-long model, other Google results are from video enhanced.)
You can clearly see that Voicegain and Amazon started quite bit behind Google and Microsoft but have since caught up.
Google seems to have the longest development cycles with very little improvement since Sept. 2021 till very recently. Microsoft, on the other hand, releases an improved recognizer every 6 months. Our improved releases are even more frequent than that.
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.
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:
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.
“We selected Voicegain because they are accurate, affordable and easy to use. We deployed the entire platform in our datacenter in under 30 minutes.”
“We’re committed to improving learning for millions of students, so we selected Voicegain for its highly accurate and affordable transcription. We have been really happy with the product and its accuracy which has continually improved as they train their models.”
“Voicegain is amazing! They have a great ASR and a modern architecture. But what we really value is their prompt and timely support. We use both their MRCP ASR and STT APIs and they work great “
Interested in customizing the ASR or deploying Voicegain on your infrastructure?
Voicegain helps developers build awesome voice enabled apps by providing them with the most accurate, affordable and accessible Speech-to-Text platform.
