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A method of English-Chinese language recognition and its application in oral English learning system

Published: 01 February 2021 Publication History

Abstract

In this paper, Gammatone Frequency Cepstrum Coefficients (GFCC) and Shifted Delta Cepstra (SDC) hybrid model were used to extract the speech feature parameters, and Gaussian Mixture Model-Universal Background Model (GMM-UBM) was used for language recognition. Taking oral English speeches of Chinese students as a corpus, we developed an English-Chinese language recognition module and applied it to the oral English learning system. The module could identify whether the student's answer language was English, which could increase the intelligence of the learning system. The experiment results showed the language recognition system introduced in this paper had a higher recognition accuracy and enhanced the function of the learning system.

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    EITCE '20: Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering
    November 2020
    1202 pages
    ISBN:9781450387811
    DOI:10.1145/3443467
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 01 February 2021

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    Author Tags

    1. GFCC feature parameters
    2. GMM-UBM language recognition
    3. SDC feature parameters
    4. oral English learning system

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    EITCE '20 Paper Acceptance Rate 214 of 441 submissions, 49%;
    Overall Acceptance Rate 508 of 972 submissions, 52%

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