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Speech Recognition Model for Confused Thai Lanna Vocabulary Using Deep Learning Techniques

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Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 15432))

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Abstract

This research investigates the development approach to developing a speech recognition model for Thai Lanna vocabulary, explicitly addressing the challenges of confused vocabulary items in the Kham Mueang dialect. The research aims to create a robust system capable of accurately recognizing and interpreting the nuances of Northern Thai speech by utilizing deep learning techniques, particularly speech transformer models. The research is based on a unique Confused Thai Lanna Vocabulary dataset, meticulously collected between 2023 and 2024. It comprises authentic recordings from native speakers and fluent communicators. The speech recognition models - HuBERT, Wav2Vec2-TH, Wav2Vec2, and WavLM - were employed to enhance the recognition and interpretation of the Confused Thai Lanna Vocabulary. Results indicate that all models effectively adapt to the Lanna language, with HuBERT consistently outperforming across all metrics. Wav2Vec2-TH demonstrated the most balanced performance across different word categories, while WavLM excelled in recognizing training words. However, significant challenges remain in processing new words and accent variants, highlighting areas for future research. This study advances ASR technology for regional dialects and contributes to preserving linguistic diversity and promoting inclusive communication in Northern Thailand.

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Acknowledgement

This research project was supported by the Thailand Science Research and Innovation Fund and the University of Phayao. It also received support from many advisors, academics, researchers, students, and staff. The authors thank everyone for their support and cooperation in completing this research.

Moreover, thanks to Claude, ChatGPT, Sci Space, Gemini, and Perplexity Generative AI for their invaluable assistance in gathering information, reading, and providing guidance throughout this research.

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Authors and Affiliations

  1. School of Information and Communication Technology, University of Phayao, Phayao, 56000, Thailand

    Wongpanya S. Nuankaew, Pathapol Jomsawan, Thapanapong Sararat & Pratya Nuankaew

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  1. Wongpanya S. Nuankaew
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  2. Pathapol Jomsawan
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  3. Thapanapong Sararat
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  4. Pratya Nuankaew
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Corresponding author

Correspondence to Pratya Nuankaew .

Editor information

Editors and Affiliations

  1. Mahasarakham University, Mahasarakham, Thailand

    Chattrakul Sombattheera

  2. Duke Kunshan University, Kunshan, China

    Paul Weng

  3. University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Jun Pang

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Nuankaew, W.S., Jomsawan, P., Sararat, T., Nuankaew, P. (2025). Speech Recognition Model for Confused Thai Lanna Vocabulary Using Deep Learning Techniques. In: Sombattheera, C., Weng, P., Pang, J. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2024. Lecture Notes in Computer Science(), vol 15432. Springer, Singapore. https://doi.org/10.1007/978-981-96-0695-5_8

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  • DOI: https://doi.org/10.1007/978-981-96-0695-5_8

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