INTEGRATED EVALUATION OF ASR, PEER, AND TEACHER FEEDBACK IN EFL PRONUNCIATION INSTRUCTION: A CONCEPTUAL TRIANGULATION FRAMEWORK

Nurmamatova Nodira Shuhrat qizi

Authors

Nurmamatova Nodira Shuhrat qizi Orental unversititi Pasixologiya yo’nalish 2 kurs magstir talabasi

Abstract

Automatic speech recognition (ASR) technology is increasingly used in English as a Foreign Language (EFL) pronunciation practice. However, a text transcript represents a signal conversion output rather than a comprehensive assessment of pronunciation or communicative competence. This critical review synthesizes empirical evidence on ASR, peer feedback, and learner evaluation in L2 pronunciation. Findings indicate that ASR enhances segmental accuracy, particularly when coupled with explicit corrective feedback, sustained practice, and targeted instruction. Nevertheless, ASR effectiveness varies by phonetic target, and recognition accuracy exhibits systemic biases across speaker accents. Peer feedback provides crucial insights into listener comprehensibility, yet its value depends on peer training and task structure. To address these limitations, this paper proposes a feedback-triangulation framework that systematically integrates system, peer, and teacher evidence to guide learner decision-making. ASR is recommended as an initial diagnostic prompt within a guided feedback cycle rather than a definitive evaluator of speech correctness.

References

1. Carless, D., & Boud, D. (2018). The development of student feedback literacy: Enabling uptake of feedback. Assessment & Evaluation in Higher Education, 43(8), 1315–1325. https://doi.org/10.1080/02602938.2018.1463354

2. Dai, Y., & Wu, Z. (2023). Mobile-assisted pronunciation learning with feedback from peers and/or automatic speech recognition: A mixed-methods study. Computer Assisted Language Learning, 36(5–6), 861–884. https://doi.org/10.1080/09588221.2021.1952272

3. Derwing, T. M., & Munro, M. J. (2005). Second language accent and pronunciation teaching: A research-based approach. TESOL Quarterly, 39(3), 379–397. https://doi.org/10.2307/3588486

4. Evers, K., & Chen, S. (2022). Effects of an automatic speech recognition system with peer feedback on pronunciation instruction for adults. Computer Assisted Language Learning, 35(8), 1869–1889. https://doi.org/10.1080/09588221.2020.1839504

5. Feng, S., Halpern, B. M., Kudina, O., & Scharenborg, O. (2024). Towards inclusive automatic speech recognition. Computer Speech & Language, 84, Article 101567. https://doi.org/10.1016/j.csl.2023.101567

6. Huang, Y., Lee, A. H., & Ballinger, S. (2023). The characteristics and effects of peer feedback on second language pronunciation. Journal of Second Language Pronunciation, 9(1), 47–70. https://doi.org/10.1075/jslp.22034.hua

7. Liu, Y., Ab Rahman, F., & Mohamad Zain, F. (2025). A systematic literature review of research on automatic speech recognition in EFL pronunciation. Cogent Education, 12(1), Article 2466288. https://doi.org/10.1080/2331186X.2025.2466288