A sampling-based speaker clustering using utterance-oriented Dirichlet process mixture model and its evaluation on large-scale data

Naohiro Tawara, Tetsuji Ogawa, Shinji Watanabe, Atsushi Nakamura, Tetsunori Kobayashi

    Research output: Contribution to journalArticle

    Abstract

    An infinite mixture model is applied to model-based speaker clustering with sampling-based optimization to make it possible to estimate the number of speakers. For this purpose, a framework of non-parametric Bayesian modeling is implemented with the Markov chain Monte Carlo and incorporated in the utterance-oriented speaker model. The proposed model is called the utterance-oriented Dirichlet process mixture model (UO-DPMM). The present paper demonstrates that UO-DPMM is successfully applied on large-scale data and outperforms the conventional hierarchical agglomerative clustering, especially for large amounts of utterances.

    Original languageEnglish
    JournalAPSIPA Transactions on Signal and Information Processing
    Volume4
    DOIs
    Publication statusPublished - 2015 Oct 28

    Keywords

    • Gibbs sampling
    • Non-parametric Bayesian model
    • Sampling approach
    • Speaker clustering
    • Utterance-oriented Dirichlet process mixture model

    ASJC Scopus subject areas

    • Information Systems
    • Signal Processing

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