Filter Bank Design for Speaker Diarization Based on Genetic Algorithms

Speech recognition systems usually need a feature extraction stage aiming at obtaining the best signal representation. In this article we propose to use genetic algorithms to design a feature extraction method adapted to the speaker diarization task. We present an adaptation of the common MFCC feature extractor which consists in designing a filter bank, with optimized bandwidths. Experiments are carried out using a state-of-the-art speaker diarization system. The proposed method outperforms the original filter bank based on the Mel scale one. Furthermore, the obtained filter bank reveals the importance of some specific spectral information for speaker recognition