REFERENCE MICROPHONE SELECTION AND LOW-RANK APPROXIMATION BASED MULTICHANNEL WIENER FILTER WITH APPLICATION TO SPEECH RECOGNITION

For multichannel speech recognition systems, it is necessary to use a speech enhancement module to suppress ambient noises. Given second-order statistics, the multichannel Wiener filter (MWF) can be designed for noise reduction. It was shown that the MWF noise reduction performance depends on the selection of reference microphone and the rank of the speech correlation matrix. It is questionable how the reference microphone and rank would affect the subsequent recognition accuracy. In this paper, we present an experimental study on the low-rank approximation and reference microphone selection based MWF with application to noisy speech recognition. Further, we propose to maximize the input signal-to-noise ratio (SNR) for reference selection in the sense of signal quality. Experimental results show that the output SNR of rank-1 MWF is independent of the reference, while the speech intelligibility is always related to both the rank and reference microphone. The word error rate is positively affected by the rank, and the proposed reference selection method can improve the performance in terms of both speech intelligibility and speech recognition.