English listening and speaking training data processing system and method based on artificial intelligence

Through the English listening and speaking training data processing system based on artificial intelligence, the LEAN neural network model is used to identify and analyze dialect sounds and generate training reference standard data, which solves the problem of the English listening and speaking training system processing dialect sounds and improves the standardization of students' English accents.

CN119649799APending Publication Date: 2025-03-18WEIHAI OCEAN VOCATIONAL COLLEGE +1
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Patent Information

Application Number
CN202411700394.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-26
Publication Date
2025-03-18

AI Technical Summary

Technical Problem

The existing English listening and speaking training system is difficult to effectively process English listening and speaking training data with dialect accents, resulting in students' English accents being unstandard.

Method used

Using an English listening and speaking training data processing system based on artificial intelligence, we use a collection of English speaking audio data of various dialect sound types and non-dial sound types, and build a LEAN neural network model, and use audio type recognition and feature extraction of listening and speaking audio data, and generate training reference standard data through clustering operations.

Benefits of technology

It realizes accurate identification and analysis of English listening and speaking training data with dialect sounds, helping trainers to correct pronunciation problems in a targeted manner and improve the standardization of English accents.

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Abstract

The invention discloses an English listening and speaking training data processing system and an English listening and speaking training data processing method based on artificial intelligence, and relates to the field of data processing. Dialect category labels of each row of data in a preprocessed first oral English audio data matrix are generated, so that a classification standard is provided for subsequently constructing an LEAN neural network model; by constructing a final LEAN neural network model, an identification model is provided for subsequent dialect sound identification of collected listening and speaking audio data, and then targeted analysis is performed on the listening and speaking audio data with dialect sound, so that a trainer is helped to correct; characteristic extraction is carried out on listening and speaking audio data corresponding to audio category label data with the maximum occurrence probability, extracted characteristic data is compared with characteristic data of source audio, clustering operation is carried out on comparison results, and the comparison data of the category with the maximum data size serves as training reference data. And the reference data is more credible and accurate.
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