MODEL-BASED DEREVERBERATION IN THE LOGMELSPEC DOMAIN FOR ROBUST DISTANT-TALKING SPEECH RECOGNITION

The REMOS (REverberation MOdeling for Speech recognition) concept for reverberation-robust distant-talking speech recognition, introduced in [1] for melspectral features, is extended in this contribution to logarithmic melspectral (logmelspec) features. Based on a combined acoustic model consisting of a hidden Markov model network and a reverberation model, REMOS determines clean-speech and reverberation estimates during recognition by an inner optimization operation. A reformulation of this inner optimization problem for logmelspec features, allowing an efficient solution by nonlinear optimization algorithms, is derived in this paper so that an efficient implementation of REMOS for logmelspec features becomes possible. Connected digit recognition experiments show that the proposed REMOS implementation significantly outperforms reverberantly-trained HMMs in highly reverberant environments.