標題: Discriminative training of Gaussian mixture bigram models with application to Chinese dialect identification
作者: Tsai, WH
Chang, WW
電信工程研究所
Institute of Communications Engineering
關鍵字: Gaussian mixture bigram model;minimum classification error algorithm;Chinese dialect identification
公開日期: 1-Mar-2002
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.
URI: http://dx.doi.org/10.1016/S0167-6393(00)00090-X
http://hdl.handle.net/11536/28962
ISSN: 0167-6393
DOI: 10.1016/S0167-6393(00)00090-X
期刊: SPEECH COMMUNICATION
Volume: 36
Issue: 3-4
起始頁: 317
結束頁: 326
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