Integrating an ASR-based translator into individualized L2 vocabulary learning for young children

Ling, Li; Chen, Weiying

doi:10.1007/s10639-022-11204-3

Cited by 5 publications

(2 citation statements)

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“…The technology’s ability to detect pronunciation errors that may have been missed by human evaluators suggests its potential to offer learners more comprehensive and targeted feedback. This finding resonates with prior research demonstrating the advantages of ASR technology in providing feedback within language learning contexts ( Ling and Chen, 2023 ). Furthermore, participants emphasized the personalized feedback provided by the ASR software, enabling them to concentrate on improving specific areas of their pronunciation.…”

Section: Discussionsupporting

confidence: 89%

The impact of automatic speech recognition technology on second language pronunciation and speaking skills of EFL learners: a mixed methods investigation

Sun

2023

Front. Psychol.

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IntroductionThis study employed an explanatory sequential design to examine the impact of utilizing automatic speech recognition technology (ASR) with peer correction on the improvement of second language (L2) pronunciation and speaking skills among English as a Foreign Language (EFL) learners. The aim was to assess whether this approach could be an effective tool for enhancing L2 pronunciation and speaking abilities in comparison to traditional teacher-led feedback and instruction.MethodsA total of 61 intermediate-level Chinese EFL learners were randomly assigned to either a control group (CG) or an experimental group (EG). The CG received conventional teacher-led feedback and instruction, while the EG used ASR technology with peer correction. Data collection involved read-aloud tasks, spontaneous conversations, and IELTS speaking tests to evaluate L2 pronunciation and speaking skills. Additionally, semi-structured interviews were conducted with a subset of the participants to explore their perceptions of the ASR technology and its impact on their language learning experience.ResultsThe quantitative analysis of the collected data demonstrated that the EG outperformed the CG in all measures of L2 pronunciation, including accentedness and comprehensibility. Furthermore, the EG exhibited significant improvements in global speaking skill compared to the CG. The qualitative analysis of the interviews revealed that the majority of the participants in the EG found the ASR technology to be beneficial in enhancing their L2 pronunciation and speaking abilities.DiscussionThe results of this study suggest that the utilization of ASR technology with peer correction can be a potent approach in enhancing L2 pronunciation and speaking skills among EFL learners. The improved performance of the EG compared to the CG in pronunciation and speaking tasks demonstrates the potential of incorporating ASR technology into language learning environments. Additionally, the positive feedback from the participants in the EG underscores the value of using ASR technology as a supportive tool in language learning classrooms.

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Section: Discussionsupporting

confidence: 89%

The impact of automatic speech recognition technology on second language pronunciation and speaking skills of EFL learners: a mixed methods investigation

Sun

2023

Front. Psychol.

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“…Other systems like (Ateeq and Hanani 2019) perform an automatic grammatical evaluation of English speech, by prompting the learner to a question in his native language and analysing the spoken response in English, by means of several extracted features that allow a language grammar assessment and meaning errors. In a more original way, the system developed by (Ling and Chen 2023) uses an ASR translator-the "Speak and Translate" app included in iPhone and iPad, among others-in which the child pronounces the word in their L1 language and the system returns the same word in L2 so that the child can then utter the L2 word. The system was tested by Australian children learning Chinese.…”

Section: Grammar and Lexical Assessmentmentioning

confidence: 99%

Automatic Speech Recognition in L2 Learning: A Review Based on PRISMA Methodology

Farrús

2023

Languages

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The language learning field is not exempt from benefiting from the most recent techniques that have revolutionised the field of speech technologies. L2 learning, especially when it comes to learning some of the most spoken languages in the world, is increasingly including more and more automated methods to assess linguistics aspects and provide feedback to learners, especially on pronunciation issues. On the one hand, only a few of these systems integrate automatic speech recognition as a helping tool for pronunciation assessment. On the other hand, most of the computer-assisted language pronunciation tools focus on the segmental level of the language, providing feedback on specific phonetic pronunciation, and disregarding the suprasegmental features based on intonation, among others. The current review, based on the PRISMA methodology for systematic reviews, overviews the existing tools for L2 learning, classifying them in terms of the assessment level, (grammatical, lexical, phonetic, and prosodic), and trying the explain why so few tools are nowadays dedicated to evaluate the intonational aspect. Moreover, the review also addresses the existing commercial systems, as well as the existing gap between those tools and the research developed in this area. Finally, the manuscript finishes with a discussion of the main findings and foresees future lines of research.

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Using high variability phonetic training as a contextualized tool in the development of English consonant clusters pronunciation among Saudi EFL learners

2023

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Integrating an ASR-based translator into individualized L2 vocabulary learning for young children

Cited by 5 publications

References 50 publications

The impact of automatic speech recognition technology on second language pronunciation and speaking skills of EFL learners: a mixed methods investigation

The impact of automatic speech recognition technology on second language pronunciation and speaking skills of EFL learners: a mixed methods investigation

Automatic Speech Recognition in L2 Learning: A Review Based on PRISMA Methodology

Using high variability phonetic training as a contextualized tool in the development of English consonant clusters pronunciation among Saudi EFL learners

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