Title: Application of neural network technology in English speech recognition and its impact on English speaking teaching

Authors: Fengxiang Zhang; Feifei Wang

Addresses: College of Foreign Languages, Hebei University of Economics and Business, Shijiazhuang, Hebei, China ' College of Foreign Languages, Hebei University of Economics and Business, Shijiazhuang, Hebei, China

Abstract: In order to improve the accuracy of English speech recognition and promote the improvement of pronunciation accuracy in English oral teaching, this paper studies the application of neural network technology in English speech recognition and its impact on oral teaching. Using Mel frequency cepstral coefficients to extract audio features of English speech signals, taking the extracted audio features as input, and based on the English speech recognition results, a BP neural network is used to construct an English speech recognition model, which outputs the English speech recognition results with the minimum cumulative residual. The impact of this technology on English oral teaching is analysed from four aspects: improving pronunciation accuracy, achieving personalised learning, enhancing interactivity and expanding learning resources. The experimental results show that the accuracy of the English speech recognition method proposed in this paper always remains above 92%, which can improve the accuracy of English oral pronunciation.

Keywords: neural network technology; English speech recognition; English speaking teaching; Mel frequency cepstral coefficient.

DOI: 10.1504/IJCAT.2026.151382

International Journal of Computer Applications in Technology, 2026 Vol.78 No.1, pp.94 - 100

Received: 19 Jun 2024
Accepted: 02 Jan 2025
Published online: 26 Jan 2026 *

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