<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the continuous development of computer technology and the deepening of the popularization of Mandarin, the role of computer technology in language-assisted language learning and recognition has become more and more significant. In this study, the acoustic model and speech model based on the Hidden Markov Model are constructed for the error detection of reading aloud in Mandarin. Then the feature extraction of the acoustics of speech signals is carried out to build the error detection model of reading aloud in Mandarin based on pronunciation features. On this basis, a DNNHMM hybrid model is built by combining deep neural networks and Hidden Markov Models for detecting Mandarin read aloud keyword errors. Through the empirical analysis of Mandarin reading error detection, it can be seen that the average accuracy of the Mandarin reading error detection model constructed in this paper is 92.37%. Compared with the other models, the average accuracy improvement ranges from 4.69% to 8.19%, and the average accuracy of the vowel and consonant pronunciation features is 85.04% and 81.69%, respectively. In the detection of errors in Mandarin reading aloud, the F-score of misreading, back-reading, adding, changing, omitting, and other six error types is above 80%, and the accuracy rate is above 83%. It shows that the deep learning-based model for reading aloud in Mandarin performs well and provides an effective method for error detection in reading aloud in Mandarin.</p>
</abstract>

With the continuous development of computer technology and the deepening of the popularization of Mandarin, the role of computer technology in language-assisted language learning and recognition has become more and more significant. In this study, the acoustic model and speech model based on the Hidden Markov Model are constructed for the error detection of reading aloud in Mandarin. Then the feature extraction of the acoustics of speech signals is carried out to build the error detection model of reading aloud in Mandarin based on pronunciation features. On this basis, a DNNHMM hybrid model is built by combining deep neural networks and Hidden Markov Models for detecting Mandarin read aloud keyword errors. Through the empirical analysis of Mandarin reading error detection, it can be seen that the average accuracy of the Mandarin reading error detection model constructed in this paper is 92.37%. Compared with the other models, the average accuracy improvement ranges from 4.69% to 8.19%, and the average accuracy of the vowel and consonant pronunciation features is 85.04% and 81.69%, respectively. In the detection of errors in Mandarin reading aloud, the F-score of misreading, back-reading, adding, changing, omitting, and other six error types is above 80%, and the accuracy rate is above 83%. It shows that the deep learning-based model for reading aloud in Mandarin performs well and provides an effective method for error detection in reading aloud in Mandarin.

Deep Learning Based Algorithm for Detecting Errors in Mandarin Read-Aloud Backreading Omission Incremental Reading

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the continuous development of computer technology and the deepening of the popularization of Mandarin, the role of computer technology in...

Deep Learning Based Algorithm for Detecting Errors in Mandarin Read-Aloud Backreading Omission Incremental Reading

Online veröffentlicht: 30. Mai 2024

Seitenbereich: -

Eingereicht: 14. Feb. 2024

Akzeptiert: 28. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1277

Schlüsselwörter
Hidden Markov Model, DNN-HMM, Feature extraction, Error detection model, Mandarin reading aloud

© 2024 Huixu Li, et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Deep Learning Based Algorithm for Detecting Errors in Mandarin Read-Aloud Backreading Omission Incremental Reading

Online veröffentlicht: 30. Mai 2024

Seitenbereich: -

Eingereicht: 14. Feb. 2024

Akzeptiert: 28. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1277

SchlüsselwörterHidden Markov Model, DNN-HMM, Feature extraction, Error detection model, Mandarin reading aloud

© 2024 Huixu Li, et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Hidden Markov Model, DNN-HMM, Feature extraction, Error detection model, Mandarin reading aloud