Abstract
This paper demonstrates that modelling session variability during GMM training can improve the performance of a GMM supervector SVM speaker verification system. Recently, a method of modelling session variability in GMM-UBM systems has led to significant improvements when the training and testing conditions are subject to session effects. In this work, session variability modelling is applied during the extraction of GMM supervectors prior to SVM speaker model training and classification. Experiments performed on the NIST 2005 corpus show major improvements over the baseline GMM supervector SVM system.
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References
Reynolds, D.A., Quatieri, T.F., Dunn, R.B.: Speaker Verification Using Adapted Gaussian Mixture Models. Digital Signal Processing 10(1), 19–41 (2000)
Gauvain, J.L., Lee, C.H.: Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains. IEEE Transactions on Speech and Audio Processing 2(2), 291–298 (1994)
Przybocki, M., Martin, A.: NIST Speaker Recognition Evaluation Chronicles. In: Odyssey Workshop (2004)
Kenny, P., Dumouchel, P.: Experiments in speaker verification using factor analysis likelihood ratios. In: Odyssey: The Speaker and Language Recognition Workshop, pp. 219–226 (2004)
Vogt, R., Sridharan, S.: Experiments in Session Variability Modelling for Speaker Verification. IEEE International Conference on Acoustics, Speech and Signal Processing 1, 897–900 (2006)
Auckenthaler, R., Carey, M., Lloyd-Thomas, H.: Score normalization for text-independent speaker verification systems. Digital Signal Processing 10(1), 42–54 (2000)
Pelecanos, J., Sridharan, S.: Feature warping for robust speaker verification. In: Proc. Speaker Odyssey 2001 (2001)
Campbell, W.M., Sturim, D.E., Reynolds, D.A.: Support vector machines using GMM supervectors for speaker verification. Signal Processing Letters 13(5), 308–311 (2006)
Vogt, R.: Automatic Speaker Recognition Under Adverse Conditions. PhD thesis, Queensland University of Technology, Brisbane, Queensland (2006)
Reynolds, D.A.: Comparison of background normalization methods for text-independent speaker verification. Proc. Eurospeech 97 (1997)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)
Campbell, W.M., Sturim, D.E., Reynolds, D.A., Solomonoff, A.: SVM Based Speaker Verification using a GMM Supervector Kernel and NAP Variability Compensation. IEEE International Conference on Acoustics, Speech and Signal Processing 1, 97–100 (2006)
The NIST 2006 Speaker Recognition Evaluation Plan (2006), Available at http://www.nist.gov/speech/tests/spk/2004/SRE-04_evalplan-v1a.pdf
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
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McLaren, M., Vogt, R., Sridharan, S. (2007). SVM Speaker Verification Using Session Variability Modelling and GMM Supervectors. In: Lee, SW., Li, S.Z. (eds) Advances in Biometrics. ICB 2007. Lecture Notes in Computer Science, vol 4642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74549-5_112
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DOI: https://doi.org/10.1007/978-3-540-74549-5_112
Publisher Name: Springer, Berlin, Heidelberg
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