NDLI: Gibbs sampling based Multi-scale Mixture Model for speaker clustering

Content Provider	IEEE Xplore Digital Library
Author	Watanabe, S. Mochihashi, D. Hori, T. Nakamura, A.
Copyright Year	2011
Description	Author affiliation: NTT Communication Science Laboratories, NTT Corporation, 2-4, Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0237, Japan (Watanabe, S.; Mochihashi, D.; Hori, T.; Nakamura, A.)
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 $(M^{3}).$ 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.
Starting Page	4524
Ending Page	4527
File Size	395460
Page Count	4
File Format	PDF
ISBN	9781457705380
ISSN	15206149
e-ISBN	9781457705397
e-ISBN	9781457705373
DOI	10.1109/ICASSP.2011.5947360
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-22
Publisher Place	Czech Republic
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Equations Bayesian methods Mathematical model speaker clustering Fully Bayesian approach Gibbs sampling multi-scale mixture model Gaussian mixture
Content Type	Text
Resource Type	Article

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