Gibbs sampling based multi-scale mixture model for speaker clustering

Shinji Watanabe, Daichi Mochihashi, Takaaki Hori, Atsushi Nakamura

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

9 Citations (Scopus)

Abstract

The aim of this work is to apply a sampling approach to speech modeling, and propose a Gibbs sampling based Multi-scale Mixture Model (M3). The proposed approach focuses on the multi-scale property of speech dynamics, i.e., dynamics in speech can be observed on, for instance, short-time acoustical, linguistic-segmental, and utterance-wise temporal scales. M 3 is an extension of the Gaussian mixture model and is considered a hierarchical mixture model, where mixture components in each time scale will change at intervals of the corresponding time unit. We derive a fully Bayesian treatment of the multi-scale mixture model based on Gibbs sampling. The advantage of the proposed model is that each speaker cluster can be precisely modeled based on the Gaussian mixture model unlike conventional single-Gaussian based speaker clustering (e.g., using the Bayesian Information Criterion (BIC)). In addition, Gibbs sampling offers the potential to avoid a serious local optimum problem. Speaker clustering experiments confirmed these advantages and obtained a significant improvement over the conventional BIC based approaches.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages4524-4527
Number of pages4
DOIs
Publication statusPublished - 2011 Aug 18
Externally publishedYes
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: 2011 May 222011 May 27

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
CountryCzech Republic
CityPrague
Period11/5/2211/5/27

Keywords

  • Fully Bayesian approach
  • Gaussian mixture
  • Gibbs sampling
  • multi-scale mixture model
  • speaker clustering

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Watanabe, S., Mochihashi, D., Hori, T., & Nakamura, A. (2011). Gibbs sampling based multi-scale mixture model for speaker clustering. In 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings (pp. 4524-4527). [5947360] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2011.5947360