A binaural speech processing method using subband-cross correlation analysis for noise robust recognition

This paper describes an extended subband-cross-correlation (SBXCOR) analysis to improve the robustness against noise. The SBXCOR analysis, which has been already proposed, is a binaural speech processing technique using two input signals and extracts the periodicities associated with the inverse of the center frequency (CF) in each subband. In this paper, by taking an exponentially weighted sum of crosscorrelation at the integral multiples of the inverse of CF, SBXCOR is extended so as to capture more periodicities included in two input signals. The experimental results using a DTW word recognizer showed that the processing improves the performance of SBXCOR for both that of the white noise and a computer room noise. For white noise, the extended SBXCOR performed significantly better than the smoothed group delay spectrum and the mel-frequency cepstral coefficient (MFCC) extracted from both monaural and binaural signals. However, for the computer room noise, it outperformed only at SNR 0 dB