A corpus-based online Mandarin pronunciation learning system for Cantonese learners: Development, evaluation, and implementation

Hsueh Chu CHEN, Qianwen HAN

Research output: Chapter in Book/Report/Conference proceedingChapters

Abstract

Hong Kong is a metropolitan city that serves as an international financial centre, giving rise to multilingual characteristics. In addition to Cantonese and English serving mostly as first and second languages, Hong Kong government encourages Mandarin as the third language under the ‘Biliteracy and Trilingualism’ language policy. This study developed a corpus-based online pronunciation learning system for Mandarin teachers, learners and researchers in order to better understand the major problems that Hong Kong Cantonese speakers encounter when learning Mandarin pronunciation. Apronunciation learning website was built based on the Mandarin spoken corpus; it contains roughly eight hours of recorded data of four spoken tasks completed by Hong Kong university students. A Mandarin tone-training program was developed using spoken data and learning resources from the learning system. The research findings will inform student learning and teaching practices and enhance teaching quality. Copyright © 2017 Author.
Original languageEnglish
Title of host publicationPTLC2017: Proceedings of the Phonetics Teaching and Learning Conference, London, 9–11 August 2017
Place of PublicationUCL, London
PublisherChandler House
Pages30-34
ISBN (Electronic)9780992639426
Publication statusPublished - 2017

Citation

Chen, H., & Han, Q. (2017). A corpus-based online Mandarin pronunciation learning system for Cantonese learners: Development, evaluation, and implementation. In PTLC2017: Proceedings of the Phonetics Teaching and Learning Conference, London, 9–11 August 2017 (pp. 30-34). UCL, London: Chandler House.

Fingerprint

Dive into the research topics of 'A corpus-based online Mandarin pronunciation learning system for Cantonese learners: Development, evaluation, and implementation'. Together they form a unique fingerprint.