Abstract
A data-driven background dataset refinement technique was recently proposed for SVM based speaker verification. This method selects a refined SVM background dataset from a set of candidate impostor examples after individually ranking examples by their relevance. This paper extends this technique to the refinement of the T-norm dataset for SVM-based speaker verification. The independent refinement of the background and T-norm datasets provides a means of investigating the sensitivity of SVM-based speaker verification performance to the selection of each of these datasets. Using refined datasets provided improvements of 13% in min. DCF and 9% in EER over the full set of impostor examples on the 2006 SRE corpus with the majority of these gains due to refinement of the T-norm dataset. Similar trends were observed for the unseen data of the NIST 2008 SRE.
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References
Kajarekar, S.S., Stolcke, A.: NAP and WCCN: Comparison of approaches using MLLR-SVM speaker verification system. In: Proc. IEEE ICASSP, pp. 249–252 (2007)
Stolcke, A., Kajarekar, S.S., Ferrer, L., Shriberg, E.: Speaker recognition with session variability normalization based on MLLR adaptation transforms. IEEE Trans. on Audio, Speech, and Language Processing 15, 1987–1998 (2007)
Auckenthaler, R., Carey, M., Lloyd-Thomas, H.: Score normalization for text-independent speaker verification systems. Digital Signal Processing 10(1), 42–54 (2000)
Burges, C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
McLaren, M., Baker, B., Vogt, R., Sridharan, S.: Improved SVM speaker verification through data-driven background dataset selection. To be presented in Proc. IEEE ICASSP (2009)
Nation Institute of Standards and Technology: NIST speech group website (2006), http://www.nist.gov/speech
Campbell, W., Reynolds, D., Campbell, J.: Fusing discriminative and generative methods for speaker recognition: Experiments on switchboard and NFI/TNO field data. In: Proc. Odyssey, pp. 41–44 (2004)
Campbell, W., Sturim, D., Reynolds, D., Solomonoff, A.: SVM based speaker verification using a GMM supervector kernel and NAP variability compensation. In: Proc. IEEE ICASSP, pp. 97–100 (2006)
McLaren, M., Vogt, R., Baker, B., Sridharan, S.: A comparison of session variability compensation techniques for SVM-based speaker recognition. In: Proc. Interspeech, pp. 790–793 (2007)
Bonastre, J., Wils, F., Meignier, S.: ALIZE, a free toolkit for speaker recognition. In: Proc. IEEE ICASSP, pp. 737–740 (2005)
Chang, C., Lin, C.: LIBSVM: A library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
NIST: The NIST Year 2008 Speaker Recognition Evaluation Plan (2008), http://www.nist.gov/speech/tests/sre/2008/sre08_evalplan_release4.pdf
Bengio, S., Mariéthoz, J.: A statistical significance test for person authentication. In: Proc. Odyssey, pp. 237–244 (2004)
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McLaren, M., Vogt, R., Baker, B., Sridharan, S. (2009). Data-Driven Impostor Selection for T-Norm Score Normalisation and the Background Dataset in SVM-Based Speaker Verification. In: Tistarelli, M., Nixon, M.S. (eds) Advances in Biometrics. ICB 2009. Lecture Notes in Computer Science, vol 5558. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01793-3_49
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DOI: https://doi.org/10.1007/978-3-642-01793-3_49
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