Open Access
June 2011 A sticky HDP-HMM with application to speaker diarization
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky
Ann. Appl. Stat. 5(2A): 1020-1056 (June 2011). DOI: 10.1214/10-AOAS395


We consider the problem of speaker diarization, the problem of segmenting an audio recording of a meeting into temporal segments corresponding to individual speakers. The problem is rendered particularly difficult by the fact that we are not allowed to assume knowledge of the number of people participating in the meeting. To address this problem, we take a Bayesian nonparametric approach to speaker diarization that builds on the hierarchical Dirichlet process hidden Markov model (HDP-HMM) of Teh et al. [J. Amer. Statist. Assoc. 101 (2006) 1566–1581]. Although the basic HDP-HMM tends to over-segment the audio data—creating redundant states and rapidly switching among them—we describe an augmented HDP-HMM that provides effective control over the switching rate. We also show that this augmentation makes it possible to treat emission distributions nonparametrically. To scale the resulting architecture to realistic diarization problems, we develop a sampling algorithm that employs a truncated approximation of the Dirichlet process to jointly resample the full state sequence, greatly improving mixing rates. Working with a benchmark NIST data set, we show that our Bayesian nonparametric architecture yields state-of-the-art speaker diarization results.


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Emily B. Fox. Erik B. Sudderth. Michael I. Jordan. Alan S. Willsky. "A sticky HDP-HMM with application to speaker diarization." Ann. Appl. Stat. 5 (2A) 1020 - 1056, June 2011.


Published: June 2011
First available in Project Euclid: 13 July 2011

zbMATH: 1232.62077
MathSciNet: MR2840185
Digital Object Identifier: 10.1214/10-AOAS395

Keywords: Bayesian nonparametrics , Hidden Markov models , hierarchical Dirichlet processes , speaker diarization

Rights: Copyright © 2011 Institute of Mathematical Statistics

Vol.5 • No. 2A • June 2011
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