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Title: 以語音辨識系統診斷高中生發音困難之評估研究
An Evaluation Study on Using an Automatic Speech Recognition System to Identify EFL Students’ Pronunciation Problems
Authors: 陳浩然
Chen, Hao Jan
Yeh, Yi-Ting
Keywords: 語音辨識
Automatic speech recognition
EFL learners
Difficult pronunciation
ASR and human ratings
Perceptions toward ASR
Issue Date: 2019
Abstract:   對英語為外語的學習者來說,英語口說能力一直都是相當重要的技能。然由於諸多限制,口說能力的涵養時常受忽略且甚少被教學。隨著語音辨識技術(即ASR,Automatic Speech Recognition)的發展,教師們開始有更多機會鍛鍊學生的口語能力。本研究之目的,即是探究學習者如何與一個載有語音辨識技術的免費網站LearnMode互動,此網站不僅讓教師能自行設計練習題,也能幫助在學生低焦慮感的環境中,逐漸熟習開口說英語。對著ASR系統練習同時,此系統也在為學習者診斷其問題發音,並以顏色標記或評語的方式,提供學習者即時的修正性回饋。在本研究中,總計有66名高中學生成為受試者,他們完成了20個根據易混淆母音/子音設計的口說任務。此外,受試者也在完成任務後填寫了一份問卷,當中10位更接受了一對一的訪談,藉此深入了解他們對於語音辨識科技的觀感與態度。   研究結果顯示,語音辨識工具與兩名老師之偵錯具有相當高的一致性,自二十個單元隨機抽樣出之五個單元中,有四個單元ASR工具與老師有超過八成的相似度。亦即,ASR工具與老師分別找出的發音錯字,有百分之八十五是相同的。此外,根據問卷與訪談結果也可得知,學生樂於在練口說時有ASR系統的協助,也因身處無同儕、師長壓力環境而更願意開口練習;不過他們仍渴望有老師在一旁,幫助他們即時改善有問題的發音。有關系統所提供的即時回饋機制,受試者認為顏色標記是有助益的,但他們也希望能有進一步的引導改善的指示。希上述研究成果能對有意協助學生提升口說能力的教師,以及計畫發展語言學習相關之語音辨識技術的研究者有綿薄貢獻。
   English speaking ability has been highly recognized as an important skill for EFL learners. Due to many constraints, though, the speaking skill cultivation is often neglected and seldom taught at school. With the developing of automatic speech recognition technologies, teachers can provide students with more opportunities to train their oral abilities. The current study investigates how learners interact with a website named LearnMode, enhanced by automatic speech technology which allows teachers to create their own speaking exercises and helps EFL learners get accustomed to speaking English under a low anxiety environment. In the meantime, the ASR system can diagnose their problematic pronunciation and offer immediate corrective feedback in the form of color highlight and comments. There are in total 66 senior high school students invited to complete 20 tasks with regard to difficult pairs of vowels and consonants. One questionnaire and one-on-one interviewed are also administered to probe into the learners’ perceptions and attitudes of the speech technologies.    The result indicates that there is a high degree of agreement of the error detection between the automatic speech recognition system and human raters. In five randomly selected units among twenty, there are four units showing that over eighty-five percent of mispronounced words locatedby ASR technology and teachers respectively are the same. Also, it is shown that learners enjoy ASR assistance and are more willing to speak English but they still want teachers to help them refine their problematic sounds. With regard to the immediate feedback mechanism, participants consider the color highlight helpful, but they would love to have further instructions on how to make the adjustment. These findings can serve as useful information for teachers who would like to incorporate speaking enhancement into their teaching and for researchers who intend to develop better ASR technologies for language teaching and learning.
Other Identifiers: G060221075L
Appears in Collections:學位論文

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