Ubiquitous Computing Framework for Reducing Ambiguity in the Lanna Thai Dialect Using Transformer Models and Fuzzy Logic

This research focuses on developing a speech-recognition model that can better handle the unique sounds and vocabulary of the Lanna Thai dialect while supporting translation between Lanna and Standard Thai. A dataset of spoken Lanna Thai was collected from native and fluent speakers between 2023 and 2025, refined from 200 selected words to 100 terms that were consistently difficult to interpret. This dataset was used to train several supervised models, including HuBERT, Wav2Vec2 (baseTH), Wav2Vec2, and WavLM, with HuBERT showing the strongest overall performance and Wav2Vec2 (baseTH) offering a balanced vocabulary response. A companion web application was also created to convert Lanna speech to text and provide two-way translation, improving access to local language resources despite some limitations with rare terms and diverse accents. Early user feedback indicates that the system is practical and helpful, supporting the broader goal of preserving Lanna Thai in a modern digital environment.

@article{Nuankaew2025,
  title     = {Ubiquitous Computing Framework for Reducing Ambiguity in the Lanna Thai Dialect Using Transformer Models and Fuzzy Logic},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Wongpanya S. Nuankaew and Pathapol Jomsawan and Pratya Nuankaew},
  doi       = {10.14569/IJACSA.2025.0161225},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161225}
}