The Potential of ASR for Improving English Pronunciation: A Review

Abstract

To pronounce well is a complex task, requiring students not only to possess knowledge of the appropriate sounds in a given context, but also to learn to use their vocal apparatus to make those sounds, equipped with extensive practice and feedback. Students in these situations require autonomous monitoring experiences to receive tailored feedback.. One of the technological tools learners can use to improve their pronunciation is Automatic Speech Recognition (ASR). This provides learners with individual practice and feedback to assist them s to accomplish their language goals. This study examines the database of research on the use of ASR in pronunciation instruction and learning available on Google Scholar, Springer Link, Education Resources Information Center (ERIC), Taylor & Francis Online and Directory of Open Access Journal (DOAJ). To help the process of identification, some procedures and criteria were employedThe results revealed that ten articles met the eligibility criteria. The procedures of utilizing ASR to improve students' pronunciation competency are then discussed in this study. 


Keywords: teaching speaking, teaching pronunciation, Automatic Speech Recognition 

References
[1] Gilakjani AP. A study of factors affecting EFL learners’ English pronunciation learning and the strategies for instruction. International Journal of Humanities and Social Science. 2012;2(3):119–128.

[2] McCrocklin SM. Pronunciation learner autonomy: The potential of Automatic Speech Recognition. System. 2016;57:25–42. https://doi.org/10.1016/j.system.2015.12.013

[3] R. M. Dauer, Accurate English: A Complete Course in Pronunciation. Hoboken: Regents/Prentice Hall, 1993.

[4] Ali S. Towards the development of a comprehensive pedagogical framework for pronunciation training based on adapted automatic speech recognition systems. Paper presented at: EURO CALL 2016: Conference on Computer Assisted Language Learning (CALL); August 24—27, 2016; Lemesos, Cyprus.

[5] van Doremalen J, Boves L, Colpaert J, Cucchiarini C, Strik H. Evaluating automatic speech recognition-based language learning systems: A case study. Computer Assisted Language Learning. 2016;29(4):833–851. https://doi.org/10.1080/09588221.2016.1167090

[6] Youn Ahn T, Lee S-M. User experience of a mobile speaking application with automatic speech recognition for EFL learning: Speaking app with ASR. British Journal of Educational Technology. 2016;47(4):778–786. https://doi.org/10.1111/bjet.12354

[7] Sidgi LF, Shaari AJ. The usefulness of automatic speech recognition (ASR) eyespeak software in improving Iraqi EFL students’ pronunciation. Advances in Language and Literary Studies (ALLS). 2017;8(1):221–226 . https://doi.org/10.7575/aiac.alls.v.8n.1p.221

[8] Elimat AK, AbuSeileek AF. Automatic speech recognition technology as an effective means for teaching pronunciation. Japan Association for Language Teaching Computer Assisted Language (JALTCALL). 2014;10(1):21–47. https://doi.org/10.29140/jaltcall.v10n1.166

[9] Sidgi LFS, Shaari AJ. The effect of automatic speech recognition eye speak software on Iraqi students’ English pronunciation: A pilot study. Advances in Language and Literary Studies (ALLS). 2017;8(2):48–54. https://doi.org/10.7575/aiac.alls.v.8n.2p.48

[10] Wallace L. Using Google web speech as a springboard for identifying personal pronunciation problems. Paper presented at: The 7th Annual Pronunciation in Second Language Learning and Teaching Conference; October 15—17, 2015; Dallas, USA.

[11] Evers K, Chen S. Effects of automatic speech recognition software on pronunciation for adults with different learning styles. Journal of Educational Computing Research. 2020;59(4):669–685. https://doi.org/10.1177/0735633120972011

[12] Evers K, Chen S. Effects of an automatic speech recognition system with peer feedback on pronunciation instruction for adults. Computer Assisted Language Learning. 2020;59(4):686–712. https://doi.org/10.1080/09588221.2020.1839504

[13] McCrocklin SM. Pronunciation learner autonomy: The potential of Automatic Speech Recognition. System. 2016;57:25–42. https://doi.org/10.1016/j.system.2015.12.013

[14] Liu X, Xu M, Li M, et al. Improving English pronunciation via automatic speech recognition technology. International Journal of Innovation and Learning. 2019;25(2): 126—140.

[15] Yuan Y, Liu X. An empirical study of the effect of ASR-supported English reading aloud practices on pronunciation accuracy. Paper presented at: The 5th International Conference on Technology in Education (ICTE 2020); August 19–22, 2020; Macau, China.

[16] Liu X, Zhu C, Jiao J, Xu M. Promoting English pronunciation via mobile devices-based automatic speech evaluation (ASE) technology. Paper presented at: The 11th International Conference on Blended Learning (ICBL 2018); 31 July—2 August 2018; Osaka, Japan.

[17] Tao R. EyeSpeak: Software review. Computer Assisted Language Instruction Consortium (CALICO) Journal. 2008;25(1):126–136.

[18] Alvaro T. EyeSpeak English software review.Jan. 2009. Accessed on: October 30, 2021. [Online]. Available: https://eyespeak-english.en.uptodown.com/windows.

[19] Liu C. Application of speech recognition technology in pronunciation correction of college oral English teaching. Paper presented at the 2020 International Conference on Multi-model Information Analytics (MMIA2020); March 5–6, 2020; Changzhou, China.