Optimizing feature complementarity by evolution strategy: Application to automatic speaker verification

Conventional automatic speaker verification systems are based on cepstral features like Mel-scale Frequency Cepstrum Coefficient (MFCC), or Linear Predictive Cepstrum Coefficient (LPCC). Recent published works showed that the use of complementary features can significantly improve the system performances. In this paper, we propose to use an evolution strategy to optimize the complementarity of two filter bank based feature extractors. Experiments we made with a state of the art speaker verification system show that significant improvement can be obtained. Compared to the standard MFCC, an Equal Error Rate (EER) improvement of 11.48% and 21,56% was obtained on the 2005 Nist SRE and Ntimit databases, respectively. Furthermore, the obtained filter banks picture out the importance of some specific spectral information for automatic speaker verification.