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.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Chetouani, M., Faundez-Zanuy, M., Gas, B., Zarader, J.L.: Non-linear Speech Feature Extraction for Phoneme Classification and Speaker Recognition. In: Chollet, G., Esposito, A., Faúndez-Zanuy, M., Marinaro, M. (eds.) Nonlinear Speech Modeling and Applications. LNCS (LNAI), vol. 3445, pp. 344–350. Springer, Heidelberg (2005)
Torkkola, K.: Feature extraction by non parametric mutual information maximization. The Journal of Machine Learning Research 3, 1415–1438 (2003)
Miyajima, C., Watanabe, H., Tokuda, K., Kitamura, T., Katagiri, S.: A new approach to designing a feature extractor in speaker identification based on discriminative feature extraction. Speech Communication 35(3-4), 203–218 (2001)
Holland, J.H.: Adaptation in natural and artificial systems. University of Michigan Press (1975)
Chin-Teng, L., Hsi-Wen, N., Jiing-Yuan, H.: Ga-based noisy speech recognition using two-dimensional cepstrum. IEEE Transactions on Speech and Audio Processing 8, 664–675 (2000)
Zamalloa, M., Bordel, G., Rodriguez, J.L., Penagarikano, M.: Feature selection based on genetic algorithms for speaker recognition. In: IEEE Odyssey, vol. 1, pp. 1–8 (2006)
Charbuillet, C., Gas, B., Chetouani, M., Zarader, J.L.: Filter bank design for speaker diarization based on genetic algorithms. In: ICASSP 2006. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2006. Proceedings, vol. 1, pp. 673–676 (2006)
Reynolds, D., Rose, R.: Robust text-independent speaker identification using gaussian mixture speaker models. IEEE Transactions on Speech and Audio Processing 3(1), 72–83 (1995)
Fine, S., Navratil, J., Gopinath, R.: A hybrid gmm/svm approach to speaker identification. In: ICASSP 2001. 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2001. Proceedings, vol. 1, pp. 417–420 (2001)
Farrell, K., Ramachandran, R., Mammone, R.: An analysis of data fusion methods for speaker verification. In: ICASSP 1998. Proceedings of the 1998 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1998, vol. 2, pp. 1129–1132 (1998)
Zhiyou, M., Yingchun, Y., Zhaohui, W.: Further feature extraction for speaker recognition. IEEE International Conference on Systems, Man and Cybernetics 5, 4153–4158 (2003)
Poh Hoon Thian, N., Sanderson, C., Bengio, S., Zhang, D., Jain Anil, K.: Spectral subband centroids as complementary features for speaker authentication. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 631–639. Springer, Heidelberg (2004)
2005 Nist SRE web site, http://www.nist.gov/speech/tests/spk/2005/
Lia spkdet web site, http://www.lia.univ-avignon.fr/heberges/ALIZE/LIA_RAL
Mitchell, T.: Machine learning. McGraw-Hill Higher Education (1997)
Paris, G., Robilliard, D., Fonlupt, C.: Exploring Overfitting in Genetic Programming. In: Liardet, P., Collet, P., Fonlupt, C., Lutton, E., Schoenauer, M. (eds.) EA 2003. LNCS, vol. 2936, pp. 267–277. Springer, Heidelberg (2004)
Yi, L., Khoshgoftaar, T.: Reducing overfitting in genetic programming models for software quality classification. In: Eighth IEEE International Symposium on High Assurance Systems Engineering, 2004. Proceedings (2004)
Ross, B.: The effects of randomly sampled training data on program evolution. In: GECCO, pp. 443–450 (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-540-77347-4_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77346-7
Online ISBN: 978-3-540-77347-4
eBook Packages: Computer ScienceComputer Science (R0)