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A Robust to Outliers Hidden Markov Model with Application in Text-Dependent Speaker Identification

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2 Author(s)
Chatzis, S. ; Electr. & Comput. Eng. Dept., Nat. Tecnical Univ. of Athens, Athens, Greece ; Varvarigou, T.

Hidden Markov models using Gaussian mixture models as their hidden state distributions have been successfully applied in text-dependent speaker identification applications. Nevertheless, it is well-known that Gaussian mixture models are very vulnerable to the presence of outliers in the fitting set used for their estimation. Student's-t mixture models have been proposed recently as a heavy-tailed, tolerant to outliers alternative to Gaussian mixture models. In this paper we exploit the robustness of student's-t mixture models in the context of hidden Markov models by introducing a new hidden Markov chain model where the hidden state distributions are student's-t mixture models. We experimentally show that our model outperforms competing text-dependent speaker identification techniques.

Published in:

Signal Processing and Communications, 2007. ICSPC 2007. IEEE International Conference on

Date of Conference:

24-27 Nov. 2007