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by Ton Ngo, Paul Van Eck, Yihong Wang, Ted Chang, Rich Hagarty | Published March 12, 2019
Artificial intelligenceDeep learningMachine learningSpeech and empathyCloud
This code pattern explains how to create a custom Watson Speech to Text model for handling specialized domain data. To improve the accuracy of the service, the code pattern uses transfer learning by training the existing model with new data from the medical industry.
The Watson Speech to Text service is among the best in the industry. However, like other Cloud speech services, it was trained with general conversational speech for general use. Therefore, it might not perform well in specialized domains such as medicine, law, or sports. To improve the accuracy of the speech-to-text service, you can use transfer learning by training the existing AI model with new data from your domain.
In this code pattern, we use a medical speech data set to illustrate the process. The data is provided by ezDI and includes 16 hours of medical dictation in both audio and text files.
When you have completed this code pattern, you will understand how to:
Find the detailed steps for this pattern in the readme file. The steps will show you how to:
When Watson Speech to Text needs a little help understanding your domain
Learn how to get your own speech with specific customization.
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