@inproceedings{basher-etal-2025-bntts,
    title = "{B}n{TTS}: Few-Shot Speaker Adaptation in Low-Resource Setting",
    author = "Basher, Mohammad Jahid Ibna  and
      Kowsher, Md  and
      Islam, Md Saiful  and
      Nandi, Rabindra Nath  and
      Prottasha, Nusrat Jahan  and
      Menon, Mehadi Hasan  and
      Muntasir, Tareq Al  and
      Chowdhury, Shammur Absar  and
      Alam, Firoj  and
      Yousefi, Niloofar  and
      Garibay, Ozlem",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-naacl.279/",
    pages = "4956--4968",
    ISBN = "979-8-89176-195-7",
    abstract = "This paper introduces BnTTS (Bangla Text-To-Speech), the first framework for Bangla speaker adaptation-based TTS, designed to bridge the gap in Bangla speech synthesis using minimal training data. Building upon the XTTS architecture, our approach integrates Bangla into a multilingual TTS pipeline, with modifications to account for the phonetic and linguistic characteristics of the language. We pretrain BnTTS on 3.85k hours of Bangla speech dataset with corresponding text labels and evaluate performance in both zero-shot and few-shot settings on our proposed test dataset. Empirical evaluations in few-shot settings show that BnTTS significantly improves the naturalness, intelligibility, and speaker fidelity of synthesized Bangla speech. Compared to state-of-the-art Bangla TTS systems, BnTTS exhibits superior performance in Subjective Mean Opinion Score (SMOS), Naturalness, and Clarity metrics."
}