Abstract
Generative Gaussian Mixture Models (GMMs) are known to be the dominant approach for modeling speech sequences in text independent speaker verification applications because of their scalability, good performance and their ability in handling variable size sequences. On the other hand, because of their discriminative properties, models like Support Vector Machines (SVMs) usually yield better performance in static classification problems and can construct flexible decision boundaries. In this paper, we try to combine these two complementary models by using Support Vector Machines to postprocess scores obtained by the GMMs. A cross-validation method is also used in the baseline system to increase the number of client scores in the training phase, which enhances the results of the SVM models. Experiments carried out on the XM2VTS and PolyVar databases confirm the interest of this hybrid approach.
The authors would like to thank the Swiss National Science Foundation for supporting this work through the National Center of Competence in Research (NCCR) on “Interactive Multimodal Information Management (IM2)”.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
S. Bengio and J. Mariéthoz. Learning the decision function for speaker verification. In IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing ICASSP, 2001.
C. J. C. Burges. A tutorial on support vector machines for pattern recognition. Data mining and Knowledge Discovery, 2(2):1–47, 1998.
G. Chollet, J.-L. Cochard, A. Constantinescu, C. Jaboulet, and P. Langlais. Swiss french polyphone and polyvar: telephone speech databases to model inter-and intra-speaker variability. IDIAP-RR 1, IDIAP, 1996.
A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum-likelihood from incomplete data via the EM algorithm. Jrnl. of Royal Statistical Society B, 39:1–38, 1977.
S. Furui. Recent advances in speaker recognition. Lecture Notes in Computer Science, 1206:237–252, 1997.
J. Mariéthoz and S. Bengio. A comparative study of adaptation methods for speaker verification. In Intl. Conf. on Spoken Language Processing ICSLP, 2002.
K. Messer, J. Matas, J. Kittler, J. Luettin, and G. Maitre. XM2VTSDB: The extended M2VTS database. In Second International Conference on Audio and Video-based Biometric Person Authentication AVBPA, March 1999.
D. A. Reynolds, T. F. Quatieri, and R. B. Dunn. Speaker verification using adapted gaussian mixture models. Digital Signal Processing, 10:19–41, 2000.
V. N. Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag, New York, NY, USA, 1995.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Le, Q., Bengio, S. (2003). Client Dependent GMM-SVM Models for Speaker Verification. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_53
Download citation
DOI: https://doi.org/10.1007/3-540-44989-2_53
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40408-8
Online ISBN: 978-3-540-44989-8
eBook Packages: Springer Book Archive