Abstract
Feature extraction/selection is an important stage in every speaker recognition system. Dimension reduction plays a mayor roll due to not only the curse of dimensionality or computation time, but also because of the discriminative relevancy of each feature. The use of automatic methods able to reduce the dimension of the feature space without losing performance is one important problem nowadays. In this sense, a method based on mutual information is studied in order to keep as much discriminative information as possible and the less amount of redundant information. The system performance as a function of the number of retained features is studied.
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References
Campbell, J.P.: Speaker recognition: A tutorial. Proceedings of the IEEE 85(9), 1437–1462 (1997)
Kinnunen, T.: Spectral features for automatic text-independent speaker recognition. Lic. Th., Department of Computer Science, University of Joensuu, Finland (2003)
Sambur, M.R.: Selection of acoustic features for speaker identification. IEEE Trans. Acoust. Speech, Signal Processing 23(2), 176–182 (1975)
Aha, D.W., Bankert, R.L.: A comparative evaluation of sequential feature selection algorithms. In: Proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, pp. 1–7. Springer, Heidelberg (1995)
Fauve, B.: Tackling Variabilities in Speaker Verification with a Focus on Short Durations. PhD thesis, School of Engineering Swansea University (2009)
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: Criteria of max-dependency, max-relevance and min-redundancy. IEEE Trans. Patt. Anal. and Mach. Intel. 27(8), 1226–1238 (2005)
Fernández, R., Montalvo, A., Calvo, J.R., Hernández, G.: Selection of the best wavelet packet nodes based on mutual information for speaker identification. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds.) CIARP 2008. LNCS, vol. 5197, pp. 78–85. Springer, Heidelberg (2008)
Bonastre, J.F., et al.: ALIZE/spkdet: a state-of-the-art open source software for speaker recognition, Odyssey, Stellenbosch, South Africa (January 2008)
Torkkola, K., Campbell, W.M.: Mutual information in learning feature transformations. In: Proc. Int. Conf. on Mach. Learning, San Francisco, CA, USA, pp. 1015–1022. Morgan Kaufmann Publishers Inc., San Francisco (2000)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley-Interscience, Hoboken (1991)
Lu, X., Dang, J.: Dimension reduction for speaker identification based on mutual information. In: Interspeech, pp. 2021–2024 (2007)
LIA_SpkDet system web site: http://www.lia.univ-avignon.fr/heberges/ALIZE/LIA_RAL
Gravier, G.: SPRO: a free speech signal processing toolkit, http://www.irisa.fr/metiss/guig/spro/
Martin, A., Doddington, G., Kamm, T., Ordowski, M., Przybocki, M.: The DET curve in assessment of detection task performance. In: Eurospeech, Rhodes, Greece, September 1997, pp. 1895–1898 (1997)
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Fernández, R., Bonastre, JF., Matrouf, D., Calvo, J.R. (2009). Feature Selection Based on Information Theory for Speaker Verification. In: Bayro-Corrochano, E., Eklundh, JO. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2009. Lecture Notes in Computer Science, vol 5856. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10268-4_36
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DOI: https://doi.org/10.1007/978-3-642-10268-4_36
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