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 h of speeches in Mongolian, resulting 30 h 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.
CITATION STYLE
Liang, K., Liu, B., Hu, Y., Liu, R., Bao, F., & Gao, G. (2023). MnTTS2: An Open-Source Multi-speaker Mongolian Text-to-Speech Synthesis Dataset. In Communications in Computer and Information Science (Vol. 1765 CCIS, pp. 318–329). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-2401-1_28
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