东南大学学报（英文版）2004，Vol.20Issue(4)：427-430,4.
基于HMM状态结构调整的非特定人语音识别
Speaker-independent speech recognition based on HMM state-restructuring method
摘要
Abstract
Based on confusions between hidden Markov model (HMM) states, a state-restructuring method is proposed. In the method, HMM states are restructured by sharing Gaussian components with their related states, and the re-estimation to the increased-parameters, i.e., the inter-state weights, is derived under the expectation maximization (EM) framework. Experiments are performed on speaker-independent, large vocabulary, continuous Mandarin speech recognition. Experimental results show that the state-restructured systems outperform the baseline, and achieve significant improvement on recognition accuracy compared with the conventional parameter-increasing method. Such comparative results confirm that the state-restructuring method is efficient.关键词
语音识别/HMM/EM算法/HTKKey words
speech recognition/hidden Markov model/expectation maximization algorithm/HMM Tookit (HTK)分类
信息技术与安全科学引用本文复制引用
徐向华,朱杰,郭强..基于HMM状态结构调整的非特定人语音识别[J].东南大学学报（英文版）,2004,20(4):427-430,4.基金项目
The Science and Technology Commission Foundation of Shanghai (No. 01JC14033). （No. 01JC14033）
