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|Title:||A robust to outliers Hidden Markov model with application in text-dependent speaker identification||Authors:||Varvarigou, Theodora
Chatzis, Sotirios P.
|Keywords:||Computing & Processing (Hardware/Software);Communication, Networking & Broadcasting;Signal processing;Gaussian processes;Markov processes;Speakers;Speech;Pattern recognition||Issue Date:||2007||Publisher:||IEEE Xplore||Source:||ICSPC 2007. IEEE international conference on signal processing and communications, 2007, Pages 804-807||Abstract:||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||URI:||http://ktisis.cut.ac.cy/handle/10488/7272||ISBN:||978-1-4244-1235-8 (print)||ISSN:||978-1-4244-1236-5 (online)
|DOI:||10.1109/ICSPC.2007.4728441||Rights:||© 2007 IEEE||Type:||Conference Papers|
|Appears in Collections:||Κεφάλαια βιβλίων/Book chapters|
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