Articulatory Modeling for Pronunciation Error Detection without Non-Native Training Data Based on DNN Transfer Learning-Reference-Cited by-同舟云学术

Articulatory Modeling for Pronunciation Error Detection without Non-Native Training Data Based on DNN Transfer Learning

Published:2017 Issue:9 Volume:E100.D Page:2174-2182
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

DUAN Richeng¹,KAWAHARA Tatsuya¹,DANTSUJI Masatake²,ZHANG Jinsong³

Affiliation:

1. Graduate School of Informatics, Kyoto University

2. Academic Center for Computing and Media Studies, Kyoto University

3. School of Information Science, Beijing Language and Culture University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E100.D/9/E100.D_2017EDP7019/_pdf

Reference50 articles.

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2. [2] C.-H. Jo, T. Kawahara, S. Doshita, and M. Dantsuji, “Automatic Pronunciation Error Detection and Guidance for Foreign Language Learning,” Proc. ICSLP, pp.2639-2642, 1998.

3. [3] W. Menzel, D. Herron, P. Bonaventura, and R. Morton, “Automatic detection and correction of non-native English pronunciations,” Proc. Speech Technology in Language Learning, pp.49-56, 2000.

4. [4] A. Neri, C. Cucchiarini, H. Strik, and L. Boves, “The pedagogy technology interface in computer assisted pronunciation training,” Proc. Computer assisted language learning, pp.441-467, 2002.

5. [5] S. Seneff, C. Wang, and J. Zhang, “Spoken conversational interaction for language learning,” Proc. InSTIL/ICALL Symposiumon on Computer Assisted Learning, pp.151-154, 2004.

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