Individual dimension gaussian mixture model for speaker identification

Authors:
Chao Wang;Li Ming Hou;Yong Fang
Affiliations:
School of Communication and Information Engineering, Shanghai, China;School of Communication and Information Engineering, Shanghai, China;School of Communication and Information Engineering, Shanghai, China
Venue:
IWBRS'05 Proceedings of the 2005 international conference on Advances in Biometric Person Authentication
Year:
2005

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Abstract

In this paper, Individual Dimension Gaussian Mixture Model (IDGMM) is proposed for speaker identification. As to the training-purpose feature vector series of a certain register, its joint probability distribution function (PDF) of is modeled by the product of the PDF of each dimension (marginal PDF), the scalar-based Gaussian Mixture Model (GMM) serving as the marginal PDF. For a good discriminative capability, the decorrelation by Schmidt orthogonalization and the Mixture Component Number (MCN) decision are adopted during the train. A close-set text-independent speaker identification experiment is also given. The simulation result shows that the IDGMM accelerates the training process remarkably and maintains the discriminative capability in testing process.