Fuzzy C-Means Clustering Based Phonetic Tied-Mixture HMM in Speech Recognition

作     者：徐向华 朱杰 郭强 XU Xiang-hua *, ZHU Jie, GUO Qiang (Dept. of Electronic Eng., Shanghai Jiaotong Univ., Shanghai 200030, China)

作者机构：Dept.of Electronic Eng. Shanghai Jiaotong Univ.Shanghai 200030China 

出 版 物：《Journal of Shanghai Jiaotong university(Science)》 (上海交通大学学报（英文版）)

年 卷 期：2005年第10卷第1期

页      面：16-20页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 

基　　金：Supported by the Science and TechnologyCommittee of Shanghai (0 1JC14 0 3 3 ) 

主　　题：speech recognition hidden Markov model (HMM) fuzzy C-means (FCM) phonetic decision tree 

摘      要：A fuzzy clustering analysis based phonetic tied-mixture HMM(FPTM) was presented to decrease parameter size and improve robustness of parameter training. FPTM was synthesized from state-tied HMMs by a modified fuzzy C-means clustering algorithm. Each Gaussian codebook of FPTM was built from Gaussian components within the same root node in phonetic decision tree. The experimental results on large vocabulary Mandarin speech recognition show that compared with conventional phonetic tied-mixture HMM and state-tied HMM with approximately the same number of Gaussian mixtures, FPTM achieves word error rate reductions by 4.84% and 13.02% respectively. Combining the two schemes of mixing weights pruning and Gaussian centers fuzzy merging, a significantly parameter size reduction was achieved with little impact on recognition accuracy.