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28 - Pronunciation

from Part VI - Language Skills and Areas

Published online by Cambridge University Press:  15 June 2025

By
Tatsuya Kawahara  and
Masatake Dantsuji
Edited by
Glenn Stockwell  and
Yijen Wang
Glenn Stockwell
Affiliation:
Waseda University, Japan
Yijen Wang
Affiliation:
Waseda University, Japan
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Summary

This chapter addresses pronunciation in second language (L2) learning, which ranges from phoneme-level pronunciation to conversation training. First, the definition of phonemes and their relationship with articulation are explained. Vowels and consonants are classified according to different dimensions. The concept of distinctive features is also described. These provide a basis to model and identify phoneme-level pronunciation errors. Suprasegmental features such as stress and rhythm are also addressed. Next, speech analysis methods are described. While formant analysis is effective for diagnosing the pronunciation of vowels, articulatory attribute detection is explored for comprehensive analysis of all phonemes. The chapter then introduces automatic speech recognition (ASR) technology to detect pronunciation errors. Settings of minimal pairs of words, prompted text, and free input can be designed. ASR models are also used for pronunciation grading. The goodness of pronunciation (GOP) score is computed for each phoneme and aggregated over all phonemes in the utterance. Nonnative speech modeling is crucial for effective L2 pronunciation learning.

Keywords

computer-assisted pronunciation learning (CAPL)intelligibilityarticulationdistinctive featuressuprasegmental featuresformant frequenciesarticulatory attributesautomatic speech recognition (ASR)goodness of pronunciation (GOP)
Type
Chapter
Information
The Cambridge Handbook of Technology in Language Teaching and Learning , pp. 460 - 478
Publisher: Cambridge University Press
Print publication year: 2025

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References

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Further Reading

This article reviews studies in a variety of areas of spoken language technology in education. It highlights the potential benefits and challenges of incorporating such technology into language learning and assessment.

While not dedicated to technology in the teaching of pronunciation, this article brings together past research to show that pronunciation has been delegated to a more minor role in communicative language teaching despite its importance. It explores how we should be teaching pronunciation and includes a discussion on how technology can contribute to improved practice in this regard.

In this article, Selieek and Elimat investigate the effectiveness of ASR in improving the pronunciation of EFL learners. The research results indicate that ASR technology has the potential to enhance learners’ performance in pronunciation by offering them accurate and timely feedback. However, the authors also recognize the necessity for additional research and development to optimize the integration of ASR into language education.

Eskenazi, M. (2009). An overview of spoken language technology for education. Speech Communication, 51, 832–844. https://doi.org/10.1016/j.specom.2009.04.005CrossRefGoogle Scholar
Pennington, M. C. (2021). Teaching pronunciation: The state of the art 2021. RELC Journal, 52(1), 3–21. https://doi.org/10.1177/00336882211002283CrossRefGoogle Scholar
Seileek, A. A., & Elimat, A. K. (2014). Automatic speech recognition technology as an effective means for teaching pronunciation. The JALT CALL Journal, 10(1), 21–47. https://doi.org/10.29140/jaltcall.v10n1.166Google Scholar

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