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(October 15)A Reverberation-Time-Aware DNN Approach to Speech Dereverberation
Oct 9, 2016


A  Reverberation-Time-Aware DNN Approach to Speech Dereverberation


Prof. Chin-Hui Lee


9:30, October 15,  2016


1602, New Science & Technology Building, North Campus

Lecturer  Profile

Professor  Chin-Hui Lee used to work at The Bell Labs. Currently, he works as a full-time  professor at Georgia Institute of Technology. Till now, he has published over  400 papers and owned 30 patents. Prof. Lee used to work as an associate editor  for IEEE Transaction on Signal Processing and IEEE Transactions on Speech and  Audio Processing. Now, he is a fellow of IEEE and ISCA.

Lecture  Abstract

We  cast the classical speech deriverberation problem into a regression setting by  mapping log power spectral features of reverberant speech to time-delayed  features of anechoic speech. Depending on the reverberation time of the  acoustic environment we found that different signal processing parameters are  needed to deliver a good quality for deriver berated speech. Furthermore,  reverberation-time-aware DNN training and decoding procedures can be designed  to optimize the deriverberation performance across a wide range of reverberation  times. In addition, a single DNN can also be trained to perform simultaneous  beam forming and deriverberation for microphone array speech. Furthermore, as a  side benefit, using DNN-based speech deriverberation as a pre-processor in the  REVERB Challenge automatic speech recognition (ASR) task, we get the lowest  word error rate without retraining the deriverberation front-end and the ASR  back-end. It is expected the ASR accuracy and robustness could still be  improved with joint training of an integrated deriverberation-ASR system.

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