@inproceedings{perera-sumanathilaka-2025-low,
    title = "A Low-Resource Speech-Driven {NLP} Pipeline for {S}inhala Dyslexia Assistance",
    author = "Perera, Peshala Sandali  and
      Sumanathilaka, Deshan Koshala",
    editor = "Angelova, Galia  and
      Kunilovskaya, Maria  and
      Escribe, Marie  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ranlp-1.106/",
    pages = "925--933",
    abstract = "Dyslexia in adults remains an under-researched and under-served area, particularly in non-English-speaking contexts, despite its significant impact on personal and professional lives. This work addresses that gap by focusing on Sinhala, a low-resource language with limited tools for linguistic accessibility. We present an assistive system designed specifically for Sinhala-speaking adults with dyslexia. The system integrates Whisper for speech-to-text conversion, SinBERT a open sourced fine-tuned BERT model trained for Sinhala to identify common dyslexic errors, and a combined mT5 and Mistral-based model to generate corrected text. Finally, the output is converted back to speech using gTTS, creating a complete multi modal feedback loop. Despite the challenges posed by limited Sinhala-language datasets, the system achieves 66{\%} transcription accuracy and 70{\%} correction accuracy with 65{\%} overall system accuracy. These results demonstrate both the feasibility and effectiveness of the approach. Ultimately, this work highlights the importance of inclusive NLP technologies in underrepresented languages and showcases a practical step toward improving accessibility for adult dyslexic users."
}