
Statistics Papers
Document Type
Journal Article
Date of this Version
2011
Publication Source
The Annals of Applied Statistics
Volume
5
Issue
2A
Start Page
1020
Last Page
1056
DOI
10.1214/10-AOAS395
Abstract
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.
Keywords
bayesian nonparametrics, hierarchical Dirichlet processes, hidden Markov models, speaker diarization
Recommended Citation
Fox, E. B., Sudderth, E. B., Jordan, M. I., & Willsky, A. S. (2011). A Sticky HDP-HMM With Application to Speaker Diarization. The Annals of Applied Statistics, 5 (2A), 1020-1056. http://dx.doi.org/10.1214/10-AOAS395
Date Posted: 27 November 2017
This document has been peer reviewed.