Speech recognition algorithm based on neural network and hidden Markov model

被引：0
作者：
Jianhui Z. [1 ]
Hongbo G. [2 ,3 ]
Yuchao L. [1 ]
Bo C. [2 ]
机构：
[1] Department of Computer Science and Technology, Tsinghua University, Beijing
[2] State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing
[3] Center for Intelligent Connected Vehicles and Transportation, Tsinghua University, Beijing
来源：
Journal of China Universities of Posts and Telecommunications | 2018年 / 25卷 / 04期
基金：
中国博士后科学基金;
关键词：
HMM; Neural network; Speech recognition;
D O I：
10.19682/j.cnki.1005-8885.2018.1014
中图分类号：
学科分类号：
摘要：
This study proposes a hybrid model of speech recognition parallel algorithm based on hidden Markov model (HMM) and artificial neural network (ANN). First, the algorithm uses HMM for time-series modeling of speech signals and calculates the voice to the HMM of the output probability score. Second, with the probability score as input to the neural network, the algorithm gets information for classification and recognition and makes a decision based on the hybrid model. Finally, Matlab software is used to train and test sample data. Simulation results show that using the strong time-series modeling ability of HMM and the classification features of the neural network, the proposed algorithm possesses stronger noise immunity than the traditional HMM. Moreover, the hybrid model corrects the individual flaws of the HMM and the neural network, and greatly improves the speed and performance of speech recognition. © 2018 Beijing University of Posts and Telecommunications. All rights reserved.
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页码：28 / 37
页数：9
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