Measuring and Improving Speech Accuracy
Automated Speech Recognition (ASR), also known as machine transcription or Speech-to-Text, uses machine learning to turn audio containing speech into text. ASR has many applications from subtitling, to virtual assistants, to IVRs, to dictation, and more. However, machine learning systems are rarely 100% accurate and ASR is no exception. If you plan to rely on ASR for critical systems it's very important to measure its accuracy or overall quality to understand how it will perform in your broader system.
Once you measure your accuracy, it’s possible to tune the systems to provide even greater accuracy for your specific situation. In Google’s Cloud Speech-to-Text API, accuracy tuning is done using our Speech Adaptation API.
With this lab and Google’s tools you can learn how to get the best possible quality from Speech-to-Text systems on your specific data.
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