Nested Gibbs sampling for mixture-of-mixture model and its application to speaker clustering

APSIPA Transactions on Signal and Information Processing

Tetsunori Kobayashi¹  Shinji Watanabe²  Naohiro Tawara¹  Tetsuji Ogawa¹ 

[1] Waseda University;Mitsubishi Electric Research Laboratories

关键词: Fully Bayesian approach;    Markov chain Monte Carlo;    Nested Gibbs sampling;    Mixture-of-mixture model;    Speaker clustering;   

DOI  :  10.1017/ATSIP.2016.15

学科分类：计算机科学（综合）

来源: Cambridge University Press

【 摘 要 】

This paper proposes a novel model estimation method, which uses nested Gibbs sampling to develop a mixture-of-mixture model to represent the distribution of the model's components with a mixture model. This model is suitable for analyzing multilevel data comprising frame-wise observations, such as videos and acoustic signals, which are composed of frame-wise observations. Deterministic procedures, such as the expectation–maximization algorithm have been employed to estimate these kinds of models, but this approach often suffers from a large bias when the amount of data is limited. To avoid this problem, we introduce a Markov chain Monte Carlo-based model estimation method. In particular, we aim to identify a suitable sampling method for the mixture-of-mixture models. Gibbs sampling is a possible approach, but this can easily lead to the local optimum problem when each component is represented by a multi-modal distribution. Thus, we propose a novel Gibbs sampling method, called “nested Gibbs sampling,” which represents the lower-level (fine) data structure based on elemental mixture distributions and the higher-level (coarse) data structure based on mixture-of-mixture distributions. We applied this method to a speaker clustering problem and conducted experiments under various conditions. The results demonstrated that the proposed method outperformed conventional sampling-based, variational Bayesian, and hierarchical agglomerative methods.

【 授权许可】

Unknown