MDL-based context-dependent subword modeling for speech recognition

Acoustical Science and Technology

Takao Watanabe¹  Koichi Shinoda¹ 

[1] NEC Corporation,4-1-1,Miyazaki,Miyamae-ku,Kawasaki,216-8555 Japan

关键词: Speech recognition;    Acoustic modeling;    Context-dependent phone;    State clustering;    MDL criterion;   

DOI  :  10.1250/ast.21.79

学科分类：声学和超声波

来源: Acoustical Society of Japan

【 摘 要 】

References(14)Cited-By(61)Context-dependent phone units, such as triphones, have recently come to be used to model subword units in speech recognition systems that are based on the use of hidden Markov models(HMMs).While most such systems employ clustering of the HMM parameters(e.g., subword clustering and state clustering)to control the HMM size, so as to avoid poor recognition accuracy due to a lack of training data, none of them provide any effective criteria for determining the optimal number of clusters.This paper proposes a method in which state clustering is accomplished by way of phonetic decision trees and in which the minimum description length(MDL)criterion is used to optimize the number of clusters.Large-vocabulary Japanese-language recognition experiments show that this method achieves higher accuracy than the maximum-likelihood approach.

【 授权许可】

Unknown