RETRACTED: Real time speech recognition algorithm on emb edded system based on continuous Markov model (Retracted article. See vol. 103, 2023)

被引:8
作者
He, Yongqiang [1 ,2 ]
Dong, Xiguang [3 ]
机构
[1] Henan Univ Engn, Coll Comp, Zhengzhou, Peoples R China
[2] Henan IoT Engn Res Ctr Smart Bldg, Zhengzhou, Peoples R China
[3] Henan Univ Engn, Coll Sci, Zhengzhou, Peoples R China
关键词
Real-time speech recognition system; Markov model; Embedded system; real-time speech vocabulary matching; algorithm; DSP signal processor; INFORMATION;
D O I
10.1016/j.micpro.2020.103058
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Real time speech recognition technology, as a key cross technology in the field of artificial intelligence in recent years, has been widely used in the fields of intelligent voice toys, industrial control and intelligent rehabilitation. Because the real-time speech recognition technology based on embedded technology has obvious advantages in the volume, power consumption and research and development cost of the system, it has become a hot carrier to achieve efficient speech recognition technology. In order to realize a simple and practical real-time speech recognition system based on embedded system, this paper designs a basic framework of machine learning based on Markov random field theory combined with machine learning theory, and studies the algorithm of real-time speech vocabulary matching recognition based on this framework. In detail, the algorithm proposed in this paper will process the speech signal from the aspects of preprocessing, signal detection, feature extraction and quantization. Finally, this paper will build a realtime speech recognition system based on DSP processor. The experimental results show that the real-time speech recognition algorithm proposed in this paper can improve the real-time recognition speed of the system. The corresponding speed changes from about 12 s to about 200 ms, and the corresponding realtime accuracy rate increases to about 95%. (C) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页数:7
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