Mitigate the reverberation effect on the speaker verification performance using different methods

Speech signals recorded in far-field or with a far receiver typically comprise additive noise and reverberation, which cause degradation and distortion in the reliability and intelligibility of speech signal, and the recognition performance of speaker recognition systems, with severe consequences in a wide range of real applications. Channel equalization, i.e. the removal or reduction or other cleaning methods of the channel effects, to some extent, mitigates the mismatching problem at the cost of added distortions to the vulnerable speech signal themselves, and therefore, its effectiveness is limited. Recent research indicates that a new speaker feature, gammatone frequency cepstral coefficients (GFCC), exhibits superior noise and reverberation robustness than other features. This paper proposed two methods to combat the effect of reverberation on speaker verification performance. The first method is using GFCC features as a robust feature to alleviate the effect of reverberation on system performance. While the second method is using multi training to combat the reverberation effect. Speaker verification experiments in the artificial and real reverberant conditions show the efficiency of the proposed methods in terms of decreased equal error rate EER and detection error trade-off DET.