Oral English Recognition Teaching System Based on Natural Language Processing and Emotional Analysis

Authors

  • Weitang Li Department of Foundation Course Teaching, Shaanxi Energy Institute, Xianyang, Shaanxi 712000, China

Abstract

The HMM model is widely used in speech recognition systems because of its high efficiency and good stability. It contains observable sequence and hidden state sequence. It is a process of finding the optimal hidden state sequence by means of an observable state set and characteristic parameters. In this paper, the author designs an oral English recognition teaching system based on natural language processing and emotional analysis. The system chooses several functions which are suitable for mobile terminal development, and provides users with basic modules for learning and practicing spoken English, including speech recognition, voice assessment, spoken broadcast and spoken dialogue. Although the endpoint detection algorithm
has been used to remove certain white noise, it has not completely eliminated the noise, and the speech recognition rate has been affected.

Keywords: Natural language processing; Emotional analysis; Oral English recognition; Training system

Cite As

W. Li, "Oral English Recognition Teaching System Based on Natural Language Processing and Emotional
Analysis", Engineering Intelligent Systems, vol. 287 no. 3, pp. 95-102, 2019.



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Published

2019-09-01

Issue

Vol. 27 No. 3 (2019): International Journal of Engineering Intelligent Systems

Section

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