Discriminative training of Gaussian mixture bigram models with application to Chinese dialect identification

Authors:
Wuei-He Tsai;Wen-Whei Chang
Affiliations:
Department of Communications Engineering, National Chiao-Tung University, Hsinchu, Taiwan, ROC;Department of Communications Engineering, National Chiao-Tung University, Hsinchu, Taiwan, ROC
Venue:
Speech Communication
Year:
2002

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Abstract

This study focuses on the parametric stochastic modeling of characteristic sound features that distinguish languages from one another. A new stochastic model, the so-called Gaussian mixture bigram model (GMBM), that allows exploitation of the acoustic feature bigram statistics without requiring transcribed training data is introduced. For greater efficiency, a minimum classification error (MCE) algorithm is employed to accomplish discriminative training of a GMBM-based Chinese dialect identification system. Simulation results demonstrate the effectiveness of the GMBM for dialect-specific acoustic modeling, and use of this model allows the proposed system to distinguish between the three major Chinese dialects spoken in Taiwan with 94.4% accuracy.