Exploring the Effects of Automated Pronunciation Evaluation on L2 Students in Thailand

Abstract

A significant barrier to effective communication in a second language is the awareness and accurate reproduction of phonetic sounds absent in the mother tongue. This study investigated whether the automated evaluation of phonetic accuracy using speech recognition technology could improve the pronunciation skills of 105 (88 female, 17 male) Thai undergraduate students studying English in Thailand. A pre-test, post-test design was employed using treatment and control sample groups, reversed over two six-week periods. Treatment group students were given access to an online platform on which they could record and submit their speech for automated evaluation and feedback via SpeechAce, a speech recognition interface designed to evaluate pronunciation and fluency. Independent samples t-test analysis of the results showed statistically significant improvement in pronunciation accuracy of students in the treatment group when compared to those in the control group (t (89) = 2.086, p = .040, 95% CI [.083, 3.423]), (t (89) = -4.692, p < .001, 95% CI [-5.157, -2.089]). Pearson’s correlation analysis indicated a weak to moderate, but statistically significant correlation between frequency of practise and pronunciation test score (r =.508, p < .001), (r = .384, p = .021). The study has limitations as the sample group was predominantly female, and time constraints limited students’ use of the software. Future studies should investigate possible gender differences and experiment with different forms of visual feedback.

Automatic Pronunciation Assessment in a Flipped Classroom Context

“…The results also revealed that the most beneficial areas of learners' improvement were pronunciation (comp. Moxon, 2021;Ngo, 2023) as well as the theoretical awareness of the issues of phonetics and phonology Setter, 2015;Wahib & Tamer, 2021;Sharipova & Kakhkhorova (2022). Learners also developed greater autonomy and study skills while participating in the course (comp.…”

“…This concept has been promoted in language learning because it allows learners to train pronunciation independently, outside the formal setting of the classroom, to achieve the goals learners see as desirable for them. Providing learners with individualized feedback when learning pronunciation is a very effective way to improve their performance (Moxon, 2021;Ngo, 2023), but it requires the capacity many teachers do not have in a regular classroom. In addition, the pronunciation practice takes place in a "safe" PUPIL: International Journal of Teaching, Education and Learning ISSN 2457-0648 34 environment, where the learner is not exposed to the evaluation of their peers, and the feedback is individualized to their particular needs (Dai & Wu, 2021;Evers & Chen, 2022).…”

Education and New Developments 2022 – Volume I

“…For instance, e.g. Moxon (2021) used automatic speech recognition software Speechace for sounds, Fouz-Gonzalez in 2017 focused on the pronunciation of particular words and, in 2020, on the accuracy of advanced learners' fossilized sounds. Zoghbor (2018) focused on LFC features, and implicitly a similar approach was taken by Al-Ahdal (2020), who corrected learners' pronunciation in discussions on TED Talks.…”

An Empirical Performance Analysis of the Speak Correct Computerized Interface

“…Moxon [21] explored whether the automated assessment of pronunciation accuracy employing speech recognition advanced technologies could enhance the pronunciation abilities of 105 Thai undergraduate learners learning English in Thailand (88 female, 17 male). A pre-test, post-test architecture was used with intervention and control sampling methods that were reversed over two six week timeframes.…”