Abstract
Speaker recognition systems usually need a feature extraction stage which aims at obtaining the best signal representation. State of the art speaker verification systems are based on cepstral features like MFCC, LFCC or LPCC. In this article, we propose a feature extraction system based on the combination of three feature extractors adapted to the speaker verification task. A genetic algorithm is used to optimise the features complementarity. This optimisation consists in designing a set of three non linear scaled filter banks. Experiments are carried out using a state of the art speaker verification system. Results show that the proposed method improves significantly the system performances on the 2005 Nist SRE Database. Furthermore, the obtained feature extractors show the importance of some specific spectral information for speaker verification.
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Charbuillet, C., Gas, B., Chetouani, M., Zarader, J.L. (2007). Multi Filter Bank Approach for Speaker Verification Based on Genetic Algorithm. In: Chetouani, M., Hussain, A., Gas, B., Milgram, M., Zarader, JL. (eds) Advances in Nonlinear Speech Processing. NOLISP 2007. Lecture Notes in Computer Science(), vol 4885. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77347-4_7
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DOI: https://doi.org/10.1007/978-3-540-77347-4_7
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