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 • Transcription for Business Users • 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
Transcribe meetings, webinars and events live using microphone or browser audio sharing. Or simply convert pre-recorded audio content into text. White-label or Source Code License of UI 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 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.
[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 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.
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:
Key observations about other results:
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.
