  We present MunTTS, an end-to-end text-to-speech (TTS) system specifically for
Mundari, a low-resource Indian language of the Austo-Asiatic family. Our work
addresses the gap in linguistic technology for underrepresented languages by
collecting and processing data to build a speech synthesis system. We begin our
study by gathering a substantial dataset of Mundari text and speech and train
end-to-end speech models. We also delve into the methods used for training our
models, ensuring they are efficient and effective despite the data constraints.
We evaluate our system with native speakers and objective metrics,
demonstrating its potential as a tool for preserving and promoting the Mundari
language in the digital age.
