Source counting in speech mixtures by nonparametric Bayesian estimation of an infinite Gaussian mixture model | IEEE Conference Publication | IEEE Xplore

Source counting in speech mixtures by nonparametric Bayesian estimation of an infinite Gaussian mixture model


Abstract:

In this paper we present a source counting algorithm to determine the number of speakers in a speech mixture. In our proposed method, we model the histogram of estimated ...Show More

Abstract:

In this paper we present a source counting algorithm to determine the number of speakers in a speech mixture. In our proposed method, we model the histogram of estimated directions of arrival with a non-parametric Bayesian infinite Gaussian mixture model. As an alternative to classical model selection criteria and to avoid specifying the maximum number of mixture components in advance, a Dirichlet process prior is employed over the mixture components. This allows to automatically determine the optimal number of mixture components that most probably model the observations. We demonstrate by experiments that this model outperforms a parametric approach using a finite Gaussian mixture model with a Dirichlet distribution prior over the mixture weights.
Date of Conference: 19-24 April 2015
Date Added to IEEE Xplore: 06 August 2015
Electronic ISBN:978-1-4673-6997-8

ISSN Information:

Conference Location: South Brisbane, QLD, Australia

References

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