Improved hidden Markov model for speech recognition and POS tagging

作     者：袁里驰 

作者机构：School of Information TechnologyJiangxi University of Finance and Economics School of Information Science and EngineeringCentral South University 

出 版 物：《Journal of Central South University》 (中南大学学报（英文版）)

年 卷 期：2012年第19卷第2期

页      面：511-516页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Project(60763001)supported by the National Natural Science Foundation of China Projects(2009GZS0027,2010GZS0072)supported by the Natural Science Foundation of Jiangxi Province,China 

主　　题：hidden Markov model Markov family model speech recognition part-of-speech tagging 

摘      要：In order to overcome defects of the classical hidden Markov model (HMM), Markov family model (MFM), a new statistical model was proposed. Markov family model was applied to speech recognition and natural language processing. The speaker independently continuous speech recognition experiments and the part-of-speech tagging experiments show that Markov family model has higher performance than hidden Markov model. The precision is enhanced from 94.642% to 96.214% in the part-of-speech tagging experiments, and the work rate is reduced by 11.9% in the speech recognition experiments with respect to HMM baseline system.