Effective Graph-Based Modeling of Articulation Traits for Mispronunciation Detection and Diagnosis-Reference-Cited by-同舟云学术

Effective Graph-Based Modeling of Articulation Traits for Mispronunciation Detection and Diagnosis

Published:2023-06-04 Issue: Volume: Page:
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Container-title:ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Yan Bi-Cheng¹,Wang Hsin-Wei¹,Wang Yi-Cheng¹,Chen Berlin¹

Affiliation:

1. National Taiwan Normal University,Taipei,Taiwan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10094559/10094560/10097226.pdf?arnumber=10097226

Reference25 articles.

1. An End-to-End Mispronunciation Detection System for L2 English Speech Leveraging Novel Anti-Phone Modeling

2. Transformer Based End-to-End Mispronunciation Detection and Diagnosis

3. Multiplex Graph Neural Network for Extractive Text Summarization

4. Semi-supervised classification with graph convolutional networks;kipf;Proc International Conference on Learning Representations (ICLR),2017

5. CNN-RNN-CTC based end-to-end mispronunciation detection and diagnosis;leung;Proc IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP),2018

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