Unsupervised learning of HMM topology for text-dependent speaker verification

Ming Liu, Thomas S Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Usually, text-dependent speaker verification can achieve better performance than text-independent system because of the constraint that the enrollment and testing utterance share the same phonetic content. However, the enrollment data for text-dependent system usually is very limited. Expectation Maximization(EM) training of HMM will suffer from noisy estimation because of limited enrollment. Adaptation is a popular solution in this scenario. The target model is formed by adapting the generic model based on limited speaker specific training data. Although the adaptation scheme can tolerate much less training data than direct EM method, the traditional method does not account the topology of HMM might be different for different speaker. The topology information further distinguish the target speaker from impostors. In this paper, we propose a unsupervised learning method to learn the topology of HMM for each speaker. The experimental results indicate that with learning the topology, the framework is more effective than traditional adaptation methods. In the pure acoustic matching experiments, the proposed method is the best system under extremely small amount enrollment data(1 training utterance) and moderate training data. That mainly due to explicitly including the label information in background modeling and discriminant capability of unsupervised learning of HMM topology.

Original languageEnglish (US)
Title of host publicationINTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP
PublisherInternational Speech Communication Association
Pages921-924
Number of pages4
Volume2
ISBN (Print)9781604234497
StatePublished - Jan 1 2006
EventINTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP - Pittsburgh, PA, United States
Duration: Sep 17 2006Sep 21 2006

Other

OtherINTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP
Country/TerritoryUnited States
CityPittsburgh, PA
Period9/17/069/21/06

Keywords

  • HMM topology
  • Speaker verification
  • Unsupervised learning

ASJC Scopus subject areas

  • Computer Science(all)

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