Utterance-based speech dereverberation using blind channel estimation and multichannel equalization

Speech dereverberation considering noisy environment as well as speaker´s movement is a challenging task. In this paper, we present an utterance-based noise-robust speech dereverberation technique that is suitable for non-stationary speaker. The acoustic impulse responses (AIRs) between the speaker and microphone array are estimated using the spectrally constrained frequency-domain least-mean-squares (LMS) algorithm. The AIRs are then equalized using the iterative multiple-input/output inverse theorem (MINT). It is assumed that the speaker stays still within an utterance, however, the speaker changes his/her position between the utterances. The simulation experiments conducted in various reverberant environment and speaker´s position demonstrate that the proposed method can satisfactorily improve the perceptual quality of the noisy reverberated speech.