LPC-BASED SPEECH DEREVERBERATION USING KALMAN-EM ALGORITHM

An algorithm for multichannel speech dereverberation is proposed that simultaneously estimates the clean signal, the linear prediction (LP) parameters of speech, and the acoustic parameters of the room. The received signals are processed in short segments to reduce the algorithm latency, and several expectation-maximization (EM) iterations are carried out on each segment to improve the signal estimation. In the expectation step, the fixed-lag Kalman smoother (FLKS) is applied to extract the clean signal from the data utilizing the estimated parameters. In the maximization step, the LP and room parameters are updated using the output of the FLKS. Experimental results show that multiple EM iterations and the application of the LP model improve the quality of the output signal.