@inproceedings{chhetri-etal-2025-impacts,
    title = "Impacts of Vocoder Selection on Tacotron-based {N}epali Text-To-Speech Synthesis",
    author = "Chhetri, Ganesh Dhakal  and
      Dahal, Kiran Chandra  and
      Poudyal, Prakash",
    editor = "Sarveswaran, Kengatharaiyer  and
      Vaidya, Ashwini  and
      Krishna Bal, Bal  and
      Shams, Sana  and
      Thapa, Surendrabikram",
    booktitle = "Proceedings of the First Workshop on Challenges in Processing South Asian Languages (CHiPSAL 2025)",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.chipsal-1.18/",
    pages = "185--192",
    abstract = "Text-to-speech (TTS) technology enhances human-computer interaction and increases content accessibility. Tacotron and other deep learning models have enhanced the naturalness of text-to-speech systems. The vocoder, which transforms mel-spectrograms into audio waveforms, significantly influences voice quality. This study evaluates Tacotron2 vocoders for Nepali text-to speech synthesis. While English language vocoders have been thoroughly examined, Nepali language vocoders remain underexplored. The study utilizes the WaveNet and MelGAN vocoders to generate speech from mel-spectrograms produced by Tacotron2 for Nepali text. In order to assess the quality of voice synthesis, this paper study the mel-cepstral distortion (MCD) and Mean Opinion Score (MOS) for speech produced by both vocoders. The comparative investigation of the Tacotron2 + MelGAN and Tacotron2 + WaveNet models, utilizing the Nepali OpenSLR and News male voice datasets, consistently reveals the advantage of Tacotron2 + MelGAN in terms of naturalness and accuracy. The Tacotron2 + MelGAN model achieved an average MOS score of 4.245 on the Nepali OpenSLR dataset and 2.885 on the male voice dataset."
}