Abstract
This paper shows a description of the system used in the ISCSLP06 Speaker Recognition Evaluation, text independent cross-channel speaker verification task. It is a discriminative Artificial Neural Network-based system, using the Non-Target Incremental Learning method to select world representatives. Two different training strategies have been followed: (i) to use world representative samples with the same channel type as the true model, (ii) to select the world representatives from a pool of samples without channel type identification. The best results have been achieved with the first alternative, but with the appearance of the additional problem of the true model channel type recognition. The system used in this task will also be shown.
This work has been supported by the Ministerio de Ciencia y Tecnología, Spain, under Project TIC2003-08382-C05-03.
This is a preview of subscription content, log in via an institution to check access.
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
Artieres, T., Bennani, Y., Gallinari, P., Montacie, C.: Connectionist and conventional models for free text talker identification. In: Proc. Neuro-Nimes, France (1991)
Batista, G.: A study of the behavior of everal methods for balancing machine learning training data. SIGKDD explorations 6(1), 20–29 (2004)
Bianchini, M., Frasconi, P., Gori, M.: Learning in multilayered networks used as autoassociators. IEEE Trans. on Neural Networks 6(2), 512–515 (1995)
Chawla, N.V., Japkowicz, N., Kotcz, A.: Editorial: Special issue on learning from imbalance data sets. SIGKDD explorations 6(1), 1–6 (2004)
Assaleh, K.T., Farrel, K.R., Mammone, R.J.: Speaker recognition using neural networks and conventional classifiers. IEEE Transactions on Speech and Audio Processing, part II 2(1) (1994)
Ganchev, T., Tasoulis, D., Vrahatis, M.N., Fakotakis, N.: Locally recurrent probabilistic neural network for text-independent speaker verification. In: Proc. Eurospeech 2003, pp. 1673–1676 (2003)
Juszczak, P., Duin, R.P.W.: Uncertainty sampling methods for oneclass classifiers. In: Proc. of the Workshop on Learning from Imbalanced Datasets II, ICML (2003)
Kramer, M.A.: Nonlinear principal component analysis using autoassociative neural networks. AIChE 37(2), 233–243 (1991)
Lapidot, I.: Som as likelihood estimator for speaker clustering. In: Proc. Eurospeech 2003, pp. 3001–3004 (2003)
Lawrence, S., Burns, I., Back, A., Tsoi, A.C., Giles, C.L.: Neural networks classification and prior class probabilities. LNCS, pp. 299–314. Springer, Heidelberg (1998)
Mansfield, A.J., Wayman, J.L.: Best pratices in testing and reporting performance of biometric devices. version 2.01. Technical report (2002)
Mary, L., Sri Rama Murty, K., Mahadeva Prasanna, S.R., Yegnanarayana, B.: Features for speaker and language identification. In: Proc. Odyssey 2004, the Speaker and Language Recognition Workshop, May 31- June 3 (2004)
Oglesby, J., Mason, J.S.: Optimization of neural models for speaker identification. In: Proceedings IEEE ICASSP, vol. S5-1, pp. 261–264 (1990)
Oglesby, J., Mason, J.S.: Radial basis function networks for speaker recognition. In: Proc. IEEE ICASSP, vol. S6.7, pp. 393–396. IEEE, Los Alamitos (1991)
Vivaracho, C.E., Ortega-Garcia, J., Alonso, L., Moro, Q.I.: Extracting the most discriminant subset from a pool of candidates to optimize discriminant classifier training. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds.) ISMIS 2003. LNCS (LNAI), vol. 2871, pp. 640–645. Springer, Heidelberg (2003)
Vivaracho, C.E., Ortega-Garcia, J., Alonso, L., Moro, Q.I.: Improving the competitiveness of discriminant neural networks in speaker verification. In: Proc. Eurospeech, September 2003, pp. 2637–2640 (2003), ISSN 1018-4074
Vivaracho-Pascual, C., Ortega-Garcia, J., Alonso-Romero, L., Moro-Sancho, Q.: A comparative study of mlp-based artificial neural networks in text-independent speaker verification against gmm-based systems. In: Lindberg, B., Dalsgaard, P., Benner, H. (eds.) Proc. of Eurospeech 2001, ISCA, September 3-7, vol. 3, pp. 1753–1756 (2001)
Wan, V., Renals, S.: Speaker recognition using sequence discriminant support vector machines. IEEE Transactions on Speech and Audio Processing 13(2), 203–210 (2005)
Yegnanarayana, B., Kishore, S.P.: Aann: an alternative to gmm for pattern recognition. Neural Networks 15(3), 459–469 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vivaracho, C.E. (2006). ISCSLP SR Evaluation, UVA–CS_es System Description. A System Based on ANNs. In: Huo, Q., Ma, B., Chng, ES., Li, H. (eds) Chinese Spoken Language Processing. ISCSLP 2006. Lecture Notes in Computer Science(), vol 4274. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11939993_55
Download citation
DOI: https://doi.org/10.1007/11939993_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49665-6
Online ISBN: 978-3-540-49666-3
eBook Packages: Computer ScienceComputer Science (R0)