Research Article
Research on Spoken English Evaluation Algorithm Based on Fuzzy Measure and Speech Recognition Technology
@INPROCEEDINGS{10.1007/978-3-031-63130-6_61, author={Maozhen Liao}, title={Research on Spoken English Evaluation Algorithm Based on Fuzzy Measure and Speech Recognition Technology}, proceedings={Application of Big Data, Blockchain, and Internet of Things for Education Informatization. Third EAI International Conference, BigIoT-EDU 2023, August 29-31, 2023, Liuzhou, China, Proceedings, Part I}, proceedings_a={BIGIOT-EDU}, year={2024}, month={7}, keywords={spoken English Speech recognition technology Fuzzy measure Evaluation algorithm}, doi={10.1007/978-3-031-63130-6_61} }- Maozhen Liao
Year: 2024
Research on Spoken English Evaluation Algorithm Based on Fuzzy Measure and Speech Recognition Technology
BIGIOT-EDU
Springer
DOI: 10.1007/978-3-031-63130-6_61
Abstract
The aim of this study is to explore English oral evaluation algorithms based on fuzzy measurement and speech recognition technology, in order to solve the problems of accuracy and speech feature extraction in traditional English oral evaluation methods. This algorithm uses principal component analysis, fuzzy measurement, and speech recognition techniques to quickly and effectively extract and evaluate oral performance. In this modern world. It is a language enriched by many new words and phrases. English is also the international language of business and diplomacy. It has become an indispensable part of one's life. English can be defined as a complex language because it contains many different types of words, such as nouns, verbs, adjectives, etc. The complexity of English makes it difficult for people to learn how to speak or write correctly without the help of teachers or books. Although the current oral pronunciation evaluation system can provide some exciting evaluation results, most of the evaluation systems focus on the acoustic characteristics of pronunciation and pay little attention to the application of specific grammar in oral English. Addition to detecting voice errors, the system can also determine whether the user has good oral skills. Voice errors are caused by users’ lack of understanding of their own language. The system can also detect a person's ability to speak other languages, such as Chinese, Japanese and Korean.
