Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Machine Learning
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
Reducing overfitting in genetic programming models for software quality classification
HASE'04 Proceedings of the Eighth IEEE international conference on High assurance systems engineering
Non-linear speech feature extraction for phoneme classification and speaker recognition
Nonlinear Speech Modeling and Applications
Evolutionary splines for cepstral filterbank optimization in phoneme classification
EURASIP Journal on Advances in Signal Processing - Special issue on biologically inspired signal processing: analyses, algorithms and applications
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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.