Environment-invariant compensation for reverberation using linear post-filtering for minimum distortion

Speaker identification systems work quite well in controlled environments but their performance degrades severely in the presence of the reverberation that is frequently encountered in realistic acoustical environments. In this paper we develop an algorithm to make speaker identification systems more robust to reverberation by passing sequences of cepstral features through a short FIR filter. The coefficients of the filter are chosen to minimize the mean square differences between compensated features in the training and testing environments. Surprisingly, the resulting filter coefficients are relatively invariant to the actual nature of the reverberation. The use of the post-filtering approach is shown to improve speaker identification accuracy, especially when reverberation times are relatively long.