Blind reverberation mitigation for robust speaker identification

摘要

Reverberation poses detrimental effects on performance of automatic speaker identification (SID) systems. This paper proposes a blind spectral weighting technique for combating the late reverberation effect (aka overlap-masking effect) on SID systems. The technique is blind in the sense that prior knowledge of neither the anechoic signal nor the room impulse response is required. Performance of the proposed technique is evaluated in terms of: 1) accuracy obtained from closed-set SID experiments, using speech material from the TIMIT corpus and four different measured room impulse responses from Aachen impulse response (AIR) database, and 2) equal-error rate (EER) obtained from experiments on a new data corpus well suited for speaker verification experiments under actual reverberant mismatched conditions, entitled MultiRoom8. Results prove that incorporating the proposed blind technique into the standard MFCC feature extraction framework yields significant improvement in SID performance.