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.

翻译：我们提出了MunTTS，一个专为蒙达里语（南亚语系下的一种低资源印度语言）设计的端到端文本转语音（TTS）系统。本研究通过收集并处理数据构建语音合成系统，填补了语言技术支持不足群体的技术空白。我们首先收集了大规模的蒙达里语文本与语音数据集，并训练了端到端语音模型。同时深入探究了模型训练方法，确保在数据受限条件下仍能保持高效性与有效性。通过母语者评估与客观指标验证，本系统展现了其在数字时代保护与推广蒙达里语的潜力。