Research on Regression Model of Multi-parameter English Pronunciation Quality Evaluation Based on Computer Technology

DOI

10.2991/978-94-6463-046-6_18How to use a DOI?

Keywords

English Pronunciation; Evaluation Model; Regression Model

Abstract

The quality of English pronunciation determines the level of spoken English. Students learning English often have trouble judging whether their pronunciation is correct. The multi-parameter-based English pronunciation quality evaluation model proposed in this paper can provide students with an easy way to use computers to independently evaluate their English pronunciation quality. Based on the previous speech recognition algorithm, this paper extracts the Mel-frequency cepstrum feature parameters in the speech signal, and uses the feature parameters as the central structure of the model to construct a model based on deep learning neural network. Within the model, the system refers to the speech rate, intonation, rhythm and intonation in the speech as evaluation treatments, and constructs an evaluation model for the quality of spoken English pronunciation based on how far regression. The model proposed in this paper can help students carry out autonomous learning and improve their learning efficiency, which is of great significance to English teaching.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jiangnan Zhang
PY  - 2022
DA  - 2022/12/17
TI  - Research on Regression Model of Multi-parameter English Pronunciation Quality Evaluation Based on Computer Technology
BT  - Proceedings of the 2022 2nd International Conference on Computer Technology and Media Convergence Design (CTMCD 2022)
PB  - Atlantis Press
SP  - 142
EP  - 148
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-046-6_18
DO  - 10.2991/978-94-6463-046-6_18
ID  - Zhang2022
ER  -