Computer-aided feedback on the pronunciation of Mandarin Chinese tones: using Praat to promote multimedia foreign language learning

Abstract

This article discusses whether digital visual and audio feedback in learners’ own voices improves their perception and production of lexical tones in Chinese as a foreign language. Forty-four beginners participated in a four-week training focused on the pronunciation of Mandarin Chinese tones at the word level. Half received digital feedback generated by Praat, a phonetic research software, to visualize the contours of their tones while hearing corrected tones in their voices. The other half participants received visual and audio feedback given by the researcher. While both groups enhanced their tone perception and production skills during the training, the Praat-aided group made significantly larger progress than the control group. Post-training survey revealed participants’ preference for the imitation of corrected tones in their voices and the direct visual comparison between their tone contours and those of corrected tones. The findings corroborate the importance of computer-assisted multimedia learning in foreign language pedagogy, which also has implications for employing research-oriented technologies and automated tonal feedback in the instruction of Chinese as a foreign language.

Keywords:

• Computer-assisted pronunciation training
• feedback
• multimedia learning
• Mandarin Chinese tone
• Praat

Acknowledgments

The author is grateful to the 44 students who participated in the study and Professor Christine Shea at the University of Iowa for constructive comments on the writing of the manuscript. Sincere thanks also go to the two reviewers who gave insightful and valuable feedback on the revision of the article.

Disclosure statement

The study is not funded by any organizations or individuals. In accordance with Taylor & Francis policies and the ethical obligation as a researcher, the author declares that there are no known competing financial interests or personal relationships that could have appeared to influence the work reported in this manuscript.

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Notes on contributors

Mengtian Chen

Mengtian Chen is a Lecturer of Chinese Language at Duke Kunshan University. She has been teaching and tutoring Chinese at both K-12 and university levels since 2011. She earned a Bachelor of Arts in teaching Chinese to speakers of other languages in 2012 and a Master of Arts in linguistics and applied linguistics in 2015 from Beijing Language and Culture University in China. She got her Doctor of Philosophy (Ph.D.) in second language acquisition from the University of Iowa in the United States in 2020, specializing in speech processing and psycholinguistics. Chen shows great passion for interdisciplinary research, particularly the psychological mechanisms of spoken language processing. Recently, her research interests have expanded into computer-assisted language instruction and teacher education.