Improved speaker recognition with recombination of multi-band features in noisy environments

Abstract

In this dissertation, we introduce a sub-band feature recombination method and also propose sub-band weighting and sub-band selection. For a more effective feature recombination, the proposed sub-band feature recombination over-comes the ineffective likelihood computation of the conventional feature recombination by using the assumption that each sub-band is independent. In this sub-band feature recombination, sub-band likelihood scores are computed by marginalization from the speaker models given multi-band feature vectors, and it is shown that the performance of this method is shown to be better than the conventional feature recombination for speaker recognition in noisy conditions. For making the sub-band feature recombination more robust to noise, the sub-band weighting and the sub-band selection are proposed. The sub-band weighting coefficients are computed based on the sub-band signal-to-noise ratio which is one of the simple and powerful criteria for sub-band reliability. In the case of applying this sub-band weighting to the sub-band feature recombination, the combination method produces average error reductions of 24.79% and 22.97% over the conventional feature recombination for speaker identification, and also for speaker verification, the average error reduction rates (ERRs) are 29.00% and 24.98% on TIMIT and NTIMIT database. Another method, the sub-band selection, which is based on likelihood scores, is proposed. The likelihood scores can also become a measure of the sub-band reliability. In the case of combining the sub-band selection with the sub-band feature recombination, the error rates are reduced by 25.63% and 9.93% for speaker identification, and for speaker verification, the average ERRs are 30.58% and 25.26% on TIMIT and NTIMIT database, respectively. In previous researches, the relative autocorrelation sequence mel-frequency cepstral coefficient (RAS-MFCC) was proposed as one of the successful features for speaker recognition in noisy env...