Text-to-Speech (TTS) synthesis for low-resource languages is an attractive
research issue in academia and industry nowadays. Mongolian is the official
language of the Inner Mongolia Autonomous Region and a representative
low-resource language spoken by over 10 million people worldwide. However,
there is a relative lack of open-source datasets for Mongolian TTS. Therefore,
we make public an open-source multi-speaker Mongolian TTS dataset, named
MnTTS2, for the benefit of related researchers. In this work, we prepare the
transcription from various topics and invite three professional Mongolian
announcers to form a three-speaker TTS dataset, in which each announcer records
10 hours of speeches in Mongolian, resulting 30 hours in total. Furthermore, we
build the baseline system based on the state-of-the-art FastSpeech2 model and
HiFi-GAN vocoder. The experimental results suggest that the constructed MnTTS2
dataset is sufficient to build robust multi-speaker TTS models for real-world
applications. The MnTTS2 dataset, training recipe, and pretrained models are
released at: https://github.com/ssmlkl/MnTTS2

本文介绍了一个名为 MnTTS2 的开源多说话者蒙古语文本转语音数据集，旨在为相关研究人员提供帮助。作者使用现代技术构建了这个 30 小时语音数据库，并开发了基于 FastSpeech2 模型的基线系统，实验结果表明该数据集可以用于构建真实世界的鲁棒多说话者 TTS 模型。