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.