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AI-Driven Dialect Classification: Enhancing Language Education with Speech Feature Detection and Deviation Scoring (94058)

Session Information:

Session: On Demand
Room: Virtual Video Presentation
Presentation Type: Virtual Presentation

All presentation times are UTC + 9 (Asia/Tokyo)

This study aims to develop and evaluate an AI-assisted pronunciation feedback tool to enhance dialectal awareness among kindergarten Hakka language teachers in Taiwan. This research investigates whether real-time, AI-generated feedback can help teachers recognize and correct their own pronunciation patterns. Due to the dominance of the other dialect and teachers’ varied phonetic backgrounds, many of them unintentionally teach a blended variant. To tackle this problem,we developed an AI-assisted pronunciation evaluation tool designed to raise dialectal awareness among language teachers. Using the Wav2Vec 2.0 model pre-trained on the LibriSpeech corpus and fine-tuned with 300 annotated Hakka sound samples focused on single words, the system provides teachers with real-time feedback on pronunciation deviations. Key evaluation metrics included accuracy, recall, precision, and F1 score, with the current model achieving 81% classification accuracy on new input data. In practice, five Hailu-speaking kindergarten teachers tested the tool. While they noted that the system's precision still needed to be improved, all participants reported increased sensitivity to dialectal differences over time. The AI feedback gradually enhanced their ability to distinguish between Sixian and Hailu pronunciations— an awareness that had previously been lacking. These findings suggest that AI tools can support professional development by helping teachers self-monitor and correct pronunciation errors before they are modeled to children. Therefore, this study demonstrates a new pathway for language teacher training in dialectally diverse settings. It could lead to establish AI-assisted pronunciation training as a pre-instructional standard, particularly for languages and dialects vulnerable to phonological convergence and loss.

Authors:
Xiang-Ling Chen, National Tsing Hua University, Taiwan
Chingching Lu, National Tsing Hua University, Taiwan


About the Presenter(s)
Xiang-Ling Chen, a second-year master's student at the Institute of Taiwan Language Research and Teaching, interested to leveraging NLP to enhance educational outcomes and contribute to the early diagnosis of cognitive disorders.

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Posted by James Alexander Gordon

Last updated: 2023-02-23 23:45:00