Complementary Features for Speaker Verification Based on Genetic Algorithms

Speech recognition systems usually need a feature extraction stage aiming at obtaining the best signal representation. State of the art speaker verification systems are based on cepstrals features like MFCC, LFCC or LPCC. In this article, we propose to use a genetic algorithm to provide new features able to complete the LFCC´s. We present an adaptation of the common LFCC feature extractor which consists in designing a filter bank, optimized for a high level fusion issue. Experiments are carried out using a state of the art speaker verification system. Results show that the proposed method improves the system performances on the 2006 Nist SRE Database.