Dereverberation Suppression for Improved Speech Recognition and Human Perception

  • Daniel J. Allred | Georgia Institute of Technology

The factors that harm the speech recognition results for un-tethered users are the ambient noise and the reverberation. While we have pretty sophisticated noise suppression algorithms, the de-reverberation is still an unsolved problem due to the difficulties in estimation and keeping track of the changes in the room response model.

Sound capturing with microphone arrays provides partial de-reverberation and ambient noise reduction due to the better directivity. This improves the speech recognition results, but still WER is higher than with a close-talk microphone.

This talk will present the results from the summer internship of Daniel Allred in MSR and is a follow-up to research done last summer. A full implementation of the dereverberation suppression algorithm designed last summer has been tested on actual room environments. We will present the possible improvements that can be achieved using our algorithm with proper parameter estimation.

We also performed some preliminary studies into the human perception of various reverberation conditions and the use of our algorithm to alleviate those conditions. Results of comparative MOS tests will be shown, and an evaluation of what these results mean for future research in dereverberation algorithms for real-time communications channels will follow.

Speaker Details

Daniel Allred is currently a 4th year PhD student at the Georgia Institute of Technology. He got his bachelor’s degree in Electrical Engineering from the University of Florida in 2001. His PhD research involves using microphone arrays with low-power hybrid analog/digital hardware architectures for improving speech capture for mobile devices. He is a member of the Cooperative Analog Digital Signal Processing (CADSP) group, headed by his advisor Prof. David Anderson and Prof. Paul Hasler.

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