|標題:||Discriminative training of Gaussian mixture bigram models with application to Chinese dialect identification|
Institute of Communications Engineering
|關鍵字:||Gaussian mixture bigram model;minimum classification error algorithm;Chinese dialect identification|
|摘要:||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. (C) 2002 Elsevier Science B.V. All rights reserved.|
|Appears in Collections:||Articles|
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