Optimization of Dereverberation Parameters based on Likelihood of Speech Recognizer

Speech recognition under reverberant condition is a difficult task. Most dereverberation techniques used to address this problem enhance the reverberant waveform independent from that of the speech recognizer. In this paper, we improve the conventional Spectral Subtraction-based (SS) dereverberation technique. In our proposed approach, the dereverberation parameters are optimized to improve the likelihood of the acoustic model. The system is capable of adaptively fine-tuning these parameters jointly with acoustic model training. Additional optimization is also implemented during decoding of the test utterances. We have evaluated using real reverberant data and experimental results show that the proposed method significantly improves the recognition performance over the conventional approach.