The potential of Automatic Speech Recognition for fostering pronunciation learners' autonomy

McCrocklin, Shannon

doi:10.31274/etd-180810-3328

Cited by 9 publications

(9 citation statements)

References 40 publications

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“…The major differences are in the corrective feedback provided and the approaches to teaching. In accordance with the needs of the learners, ASR technology is more sufficient compared to non-ASR, as the feedback from ASR technology are customized based on the ability of each learner, whereas the latter is focused on learners' answer concerning isolated sounds and short sentences and compared to a pronunciation model, even though the two utterances do not coincide [2], [39].…”

Section: B Mobile-based Technology In Pronunciation Learningmentioning

confidence: 99%

“…Previous empirical studies [38]- [42] highlighted that out of the four skills in English, speaking is the most affected one as learners are cautious in the pronunciation related to being intelligible. In line with the circumstances in recent years where technology has emerged and reinforced in educational settings due to the pandemic [16], technology enables the learners to have more practices in their own time and space where they can develop their competencies in their learning in an encouraging setting [16], [39], [42], [43], and expose to new chances for the teaching and learning improvement [17].…”

Section: Technology and Self-directed Learningmentioning

confidence: 99%

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Incorporating Mobile-based Artificial Intelligence to English Pronunciation Learning in Tertiary-level Students: Developing Autonomous Learning

Sariani

Miladiyenti²,

Rozi³

et al. 2022

ijasce

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EFL pronunciation learning is known to be teacher-centered, in which students are reliant on the feedback given by their teachers. Fortunately, the presence of technology-based learning tools is believed to be able to reduce students' dependency on teachers. A built-in Automatic Speech Recognition technology (ASR): ELSA Speak is a mobile-based application that could help students with pronunciation learning by providing prompt feedback and detailed evaluation of its users' pronunciation accuracy. The objective of this study was to examine the use of the mobile-based application, ELSA, in pronunciation learning and whether it could stimulate learners to become more autonomous. A mix-method approach was employed in this study. The quantitative data, in the forms of pre-test and post-test, was gathered through students' pronunciation recordings available in ELSA. This data was analyzed using the paired sample t-test. In addition, a Google Form questionnaire, in five-point Likert Scale, was used to support the quantitative findings. Meanwhile, a semi-structured interview with selected participants served as the qualitative one. These data were analyzed thematically based on the participants' responses. The result shows that incorporating mobile-based technology into English pronunciation learning could provide an opportunity for students to improve their pronunciation. Moreover, students could also broaden their repertoire of available practice strategies that might be useful to improve their autonomous learning ability.

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Section: B Mobile-based Technology In Pronunciation Learningmentioning

confidence: 99%

Section: Technology and Self-directed Learningmentioning

confidence: 99%

Incorporating Mobile-based Artificial Intelligence to English Pronunciation Learning in Tertiary-level Students: Developing Autonomous Learning

Sariani

Miladiyenti²,

Rozi³

et al. 2022

ijasce

View full text Add to dashboard Cite

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“…Feedback is a debatable constituent of CAPT, and it has not been investigated much; thus, its effectiveness has not been specified. However, it is of special concern for students who wish to learn English as a second language, though its role has been deliberated (McCrocklin, 2014). Most commonly, feedback is given by teachers and is fundamentally known as repetition with change.…”

Section: Feedbackmentioning

confidence: 99%

The Usefulness of Automatic Speech Recognition (ASR) Eyespeak Software in Improving Iraqi EFL Students’ Pronunciation

Sidgi

Shaari

2017

ALLS

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The present study focuses on determining whether automatic speech recognition (ASR) technology is reliable for improving English pronunciation to Iraqi EFL students. Non-native learners of English are generally concerned about improving their pronunciation skills, and Iraqi students face difficulties in pronouncing English sounds that are not found in their native language (Arabic). This study is concerned with ASR and its effectiveness in overcoming this difficulty. The data were obtained from twenty participants randomly selected from first-year college students at Al-Turath University College from the Department of English in Baghdad-Iraq. The students had participated in a two month pronunciation instruction course using ASR Eyespeak software. At the end of the pronunciation instruction course using ASR Eyespeak software, the students completed a questionnaire to get their opinions about the usefulness of the ASR Eyespeak in improving their pronunciation. The findings of the study revealed that the students found ASR Eyespeak software very useful in improving their pronunciation and helping them realise their pronunciation mistakes. They also reported that learning pronunciation with ASR Eyespeak enjoyable.

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“…Many studies recommend its use as essential to the process due to the advantages it offers learners (Chapelle, 2001;ButlerPascoe & Wiburg, 2003;Neri, Cucchiarini, & Strik, 2001;Harless, Zier, & Duncan, 1999;Kim, 2006;Pennington, 1996;McCrocklin, 2014). For instance, ASR technology gives the instructor the opportunity to discover each individual learner's problems with pronunciation.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Automatic Speech Recognition EyeSpeak Software on Iraqi Students’ English Pronunciation: A Pilot Study

Sidgi

Shaari

2017

ALLS

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The use of technology, such as computer-assisted language learning (CALL), is used in teaching and learning in the foreign language classrooms where it is most needed. One promising emerging technology that supports language learning is automatic speech recognition (ASR). Integrating such technology, especially in the instruction of pronunciation in the classroom, is important in helping students to achieve correct pronunciation. In Iraq, English is a foreign language, and it is not surprising that learners commit many pronunciation mistakes. One factor contributing to these mistakes is the difference between the Arabic and English phonetic systems. Thus, the sound transformation from the mother tongue (Arabic) to the target language (English) is one barrier for Arab learners. The purpose of this study is to investigate the effectiveness of using automatic speech recognition ASR EyeSpeak software in improving the pronunciation of Iraqi learners of English. An experimental research project with a pretest-posttest design is conducted over a one-month period in the Department of English at Al-Turath University College in Baghdad, Iraq. The ten participants are randomly selected first-year college students enrolled in a pronunciation class that uses traditional teaching methods and ASR EyeSpeak software. The findings show that using EyeSpeak software leads to a significant improvement in the students' English pronunciation, evident from the test scores they achieve after using EyeSpeak software.

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The potential of Automatic Speech Recognition for fostering pronunciation learners' autonomy

Cited by 9 publications

References 40 publications

Incorporating Mobile-based Artificial Intelligence to English Pronunciation Learning in Tertiary-level Students: Developing Autonomous Learning

Incorporating Mobile-based Artificial Intelligence to English Pronunciation Learning in Tertiary-level Students: Developing Autonomous Learning

The Usefulness of Automatic Speech Recognition (ASR) Eyespeak Software in Improving Iraqi EFL Students’ Pronunciation

The Effect of Automatic Speech Recognition EyeSpeak Software on Iraqi Students’ English Pronunciation: A Pilot Study

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