Speaker clustering based on utterance-oriented Dirichlet process mixture model

Research output: Contribution to journalConference article

6 Citations (Scopus)

Abstract

This paper provides the analytical solution and algorithm of UO-DPMM based on a non-parametric Bayesian manner, and thus realizes fully Bayesian speaker clustering. We carried out preliminary speaker clustering experiments by using a TIMIT database to compare the proposed method with the conventional Bayesian Information Criterion (BIC) based method, which is an approximate Bayesian approach. The results showed that the proposed method outperformed the conventional one in terms of both computational cost and robustness to changes in tuning parameters.

Original languageEnglish
Pages (from-to)2905-2908
Number of pages4
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publication statusPublished - 2011 Dec 1
Event12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011 - Florence, Italy
Duration: 2011 Aug 272011 Aug 31

Keywords

  • Gibbs sampling
  • Non-parametric Bayesian model
  • Speaker clustering
  • Utterance-oriented DPMM

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modelling and Simulation

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