Presentation 2014-01-23
Speaker recognition based on log-linear models using feature generation by variational Bayesian method
Akifumi TSUGE, Kei HASHIMOTO, Yoshihiko NANKAKU, Keiichi TOKUDA,
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Abstract(in English) This paper presents a speaker recognition technique based on log-linear models (LLMs) using Bayesian statistics. Since discriminative models can use various features in the unified framework, preparation of features that are useful for classification is an important problem. Statistics obtained from Gaussian Mixture Models (GMMs) trained by the maximum likelihood method or the maximum a posteriori method are recently used as features for speaker recognition. However, these training methods often occur the over-fitting problem. In this paper, the Bayesian approach is applied to train GMMs and statistics of GMMs in the Bayesian approach are used as features of LLMs. Experimental results show that the proposed LLM-based method significantly improved the identification rates from conventional GMM-based methods.
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Keyword(in English) speaker recognition / GMM / Bayesian approach / log-linear model
Paper # SP2013-98
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Committee SP
Conference Date 2014/1/16(1days)
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Registration To Speech (SP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Speaker recognition based on log-linear models using feature generation by variational Bayesian method
Sub Title (in English)
Keyword(1) speaker recognition
Keyword(2) GMM
Keyword(3) Bayesian approach
Keyword(4) log-linear model
1st Author's Name Akifumi TSUGE
1st Author's Affiliation Department of Computer Science and Engineering, Nagoya Institute of Technology()
2nd Author's Name Kei HASHIMOTO
2nd Author's Affiliation Department of Computer Science and Engineering, Nagoya Institute of Technology
3rd Author's Name Yoshihiko NANKAKU
3rd Author's Affiliation Department of Computer Science and Engineering, Nagoya Institute of Technology
4th Author's Name Keiichi TOKUDA
4th Author's Affiliation Department of Computer Science and Engineering, Nagoya Institute of Technology
Date 2014-01-23
Paper # SP2013-98
Volume (vol) vol.113
Number (no) 404
Page pp.pp.-
#Pages 6
Date of Issue