Improve English Pronunciation at Word Level for Thai EFL Learners in Southern Region Using End-to-End Automatic Speech Recognition

Abstract

ASR (Automatic Speech Recognition) is favorably chosen as a learning technology, which is used for English pronunciation practice. This research aims to build a personalized learning platform to improve English pronunciation at the word level for Thai EFL learners who learn English as a Foreign Language (EFL) by using ASR to detect mispronounced sounds. ASR models are built with an End-to-End learning approach with a Thai-English mispronounced words dataset. The practice of English pronunciation particularly focuses on eleven problematic consonant sounds of Thai EFL students according to the previous studies of English pronunciation in Thai contexts. These eleven consonant sounds are divided into five groups: 1)  /ð/-/θ/-/tθ/, 2) /ʒ/-/ʃ/, 3) /dʒ/-/tʃ/, 4) /z/-/s/ and 5) /b/-/p/. The five of Grade 12 Thai Students who are native Thai speakers were selected as sampling process. The result of pre-test and post-test show that the samples have the most problem with the consonant sounds of /ö/-19/-1t9/ (29%), followed by /b/-/p/ (22%), /d3/-/tf/ (22%), /z/-/s/ (18%) and 13/-11/ (9%) respectively. In conclusion, this study reveals that 60% of the samples have improved their pronunciation after using our system.