English Speech Scoring System Based on Computer Neural Network

Xianxian Wu; Yan Zhang

doi:10.54097/ijeh.v5i2.2143

Authors

Xianxian Wu
Yan Zhang

DOI:

https://doi.org/10.54097/ijeh.v5i2.2143

Keywords:

Evaluation of english pronunciation quality, CNN, Fourier transform, English education.

Abstract

In English phonetics teaching, in order to improve students' English phonetics quality, a computer neural network based English phonetics scoring method is proposed. First, the frequency domain spectrogram is used as the data input to construct a convolutional neural network model at the word and phoneme levels to detect speech similarity. Then the original sound time domain waveform is used as the data input, which is converted into text through neural network to detect the text difference. Finally, we combine the two with the assigned weight to give a relatively objective comprehensive pronunciation score. The simulation results show that the method is accurate and practical, and can promote the standardization of students' English pronunciation.

Downloads

Download data is not yet available.

References

Dalia Lisette Aguilar Vacacela, Maria Rossana Ramirez. “Self-awareness Strategy Using Podcasting to Improve Tense and Lax Vowel Pronunciation Sounds in Beginner EFL-Adult Learners,” Journal of Foreign Language Teaching and Learning, vol. 5, no.1, pp. 79-98, 2020.

Le Tian. “Research and Application of Interactive Teaching Strategies for College English Phonics in the Context of Internet plus,” Modern English, no.16, pp. 103-105, 2021.

S. Misirov, “The peculiarities of teaching English pronunciation in elementary classes (GRADES)[J],” Scientific Bulletin of Namangan State University, vol. 1, no. 2, p. 63, 2019.

T. Isaacs and L. Harding, “Pronunciation assessment,” Language Teaching, vol. 50, no. 3, pp. 347–366, 2017.

CHEN Xiao-hong and TENG Hua, “Research on English speech recognition based on deep machine learning,” Journal of Guiyan University Natural Sciences, vol. 16, no.3, pp.1-33, 2021.

Wenjuan He , “Automatic Error Detection Method of Embedded English Speech Teaching Recognition System under the Background of Artificial Intelligence,” Mobile Information Systems, 2022.

Ming Liu and Lei Chen, “Similarity Calculation via Passage-Level Event Connection Graph, Applied Sciences,” vol. 12, no. 9887, p.9887, 2022.

Yu Zhao, “Control System and Speech Recognition of Exhibition Hall Digital Media Based on Computer Technology,” Mobile Information Systems, vol. 2022, Article ID 7427899, 2022.

Yuyuan Zhang, Wenjun Yan, Limin Zhang, Ling Ma, “Automatic Space-Time Block Code Recognition Using Convolutional Neural Network With Multi-Delay Features Fusion,” IEEE Access, vol 9, pp. 79994 -80005, 2021.

Yanmin Yu, Yongcai Lai, Ping Yan, Haiying Liu, “The Novel Sequence Distance Measuring Algorithm Based on Optimal Transport and Cross-Attention Mechanism,” Shock and Vibration, vol. 2021, Article ID 3272119, 2021.

Yu-Yi Lin, Wei-Zhong Zheng, Wei Chung Chu, Ji-Yan Han, “A Speech Command Control-Based Recognition System for Dysarthric Patients Based on Deep Learning Technology,” Applied Sciences, vol. 11, no.2477, p. 2477, 2021.

Wan-Ju Lin, Shih-Hsuan Lo, Hong-Tsu Young, Che-Lun Hung, “Evaluation of Deep Learning Neural Networks for Surface Roughness Prediction Using Vibration Signal Analysis,” Applied Sciences, vol. 9, no. 7, p. 1462, Apr. 2019.

Youhui Tian, “Artificial Intelligence Image Recognition Method Based on Convolutional Neural Network Algorithm,” IEEE Access, vol. 8, pp. 125731-125744, Jan. 2021.

X. Zhe, S. Chen, and H. Yan, “Directional statistics-based deep metriclearning for image classification and retrieval,” Pattern Recognit., vol. 93, pp. 113–123, Sep. 2019.

Yin Hui, Nadeu Climent, Hohmann Volker, “Pitch- and Formant-Based Order Adaptation of the Fractional Fourier Transform and Its Application to Speech Recognition,” EURASIP Journal on Audio, Speech, and Music Processing, vol. 2009, no. 1, p. 304579, Jan. 2009.

Alexander Veysov, “Silero Models,” 2022. Available: https://github.com/snakers4/silero -models

Gonnella Giorgio, Kurtz Stefan, “Readjoiner: a fast and memory efficient string graph-based sequence assemble,” BMC Bioinformatics, vol. 13, no. 1 , p. 82, May. 2012