PTeacher: a Computer-Aided Personalized Pronunciation Training System with Exaggerated Audio-Visual Corrective Feedback

Author:

Bu Yaohua1,Ma Tianyi2,Li Weijun3,Zhou Hang4,Jia Jia2,Chen Shengqi2,Xu Kaiyuan2,Shi Dachuan2,Wu Haozhe2,Yang Zhihan2,Li Kun5,Wu Zhiyong6,Shi Yuanchun2,Lu Xiaobo1,Liu Ziwei7

Affiliation:

1. Academy of Arts & Design Tsinghua University, China

2. Department of Computer Science and Technology Tsinghua University, China

3. School of Information Science and Technology Northeast Normal University, China

4. Electronic Engineering The Chinese University of Hong Kong, China

5. Speech X Limited, China

6. Tsinghua University, China

7. The Chinese University of Hong Kong, China

Funder

the state key program of the National Natural Science Foundation of China (NSFC)

National Key R&D Program of China

National Key Research and Development Project of China

Publisher

ACM

Reference89 articles.

1. The impact of automatic exaggeration of the visual articulatory features of a talker on the intelligibility of spectrally distorted speech

2. Pierre Badin Atef Ben Youssef Gérard Bailly Frédéric Elisei and Thomas Hueber. 2010. Visual articulatory feedback for phonetic correction in second language learning. In Second Language Studies: Acquisition Learning Education and Technology. Pierre Badin Atef Ben Youssef Gérard Bailly Frédéric Elisei and Thomas Hueber. 2010. Visual articulatory feedback for phonetic correction in second language learning. In Second Language Studies: Acquisition Learning Education and Technology.

3. Computer-assisted visual articulation feedback in L2 pronunciation instruction

4. Training Japanese listeners to identify English /r/ and /l/: IV. Some effects of perceptual learning on speech production

5. Articulatory Phonology: An Overview

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1. Performative Vocal Synthesis for Foreign Language Intonation Practice;Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems;2023-04-19

2. RLens: A Computer-aided Visualization System for Supporting Reflection on Language Learning under Distributed Tutorship;Proceedings of the Ninth ACM Conference on Learning @ Scale;2022-06

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