Robust Speech Recognition:

Field deployment of speech recognition technology results in a number of interesting problems, such as microphone saturation, which severely limit the performance of speech recognition engines. In this project, we are developing algorithms that can postprocess the speech signal before feature extraction, and can mitigate the effects of saturation, clipping, and other forms of signal degradation. At the core of this research is an interesting information theory question of whether you can recover lost information in a speech signal using the redundancy in the signal.

Robust Recognition
 

Acoustic Model Adaptation:

Model Adaptation

This project also focuses on improving the performance of a speech recognition engine by adapting the speaker independent HMM-based acoustic models to speaker dependent acoustic models. Techniques to map models based on discrete distributions to continuous distributions are being investigated.

This project is funded by Conversational Computing Corporation (Conversay). Conversational Computing Corporation is a Redmond, WA based developer of speech technology solutions for both mobile and traditional internet access devices.


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