An Efficient HMM-Based Feature Enhancement Method With Filter Estimation for Reverberant Speech Recognition
Ji-Won Cho, Hyung‐Min Park · IEEE Signal Processing Letters · 2013
This letter presents an efficient feature enhancement method for reverberant speech recognition that derives a minimum mean square error estimate of clean logarithmic mel-frequency power spectral coefficients (LMPSCs) based on a hidden-Markov-model(HMM) prior. Although an observation model of the reverberant LMPSCs can be simply formulated by coarse modeling of the room impulse response (RIR) , the presented method estimates not only the clean LMPSCs but also the RIR to reflect detailed reverberation. The experimental results indicate that the described method can further reduce relative word error rate (WER) by 18.09% on average compared to a method based on RIR coarse modeling.