Hybrid model of hidden Markov models and wavelet neural network in noisy speech recognition

被引：0

作者：

Lin Sui-fang ^{[1
]}

Pan Yong-xiang ^{[1
]}

Sun Xu-xia ^{[1
]}

机构：

[1] Xian Univ Technol, Sch Automat & Informat Engn, Xian 710048, Peoples R China

来源：

Proceedings of 2005 Chinese Control and Decision Conference, Vols 1 and 2 | 2005年

关键词：

speech recognition; hidden Markov model; wavelet neural networks; noisy environments;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Noisy speech recognition method is presented based on a hybrid model of hidden Markov models (HMM) and wavelet neural network (WNN). The HMM is employed to establish model for speech signals and compute the Viterbi output score. The score are used as the input of WNN to acquire the classify information. The recognition decision is made by fusion two kinds of recognition information. Recognition experiment shows that the hybrid model has higher performance than hidden Markov model in noisy speech recognition for modeling ability of HMM and pattern classify ability of WNN, so, the performance of speech recognition system is improved.

引用

页码：675 / 678

页数：4