# Comparative Study for Multi-Speaker Mongolian TTS with a New Corpus
###### Abstract  
Low-resource text-to-speech synthesis is a very promising research direction. Mongolian is the official language of the Inner Mongolia Autonomous Region and is spoken by more than 10 million people worldwide. Mongolian, as a representative low-resource language, has a relative lack of open-source datasets for its TTS. Therefore, we make public an open-source multi-speaker Mongolian TTS dataset, named MnITS2, for related researchers. In this work, we invited three Mongolian announcers to record topic-rich speeches. Each announcer recorded 10 h of Mongolian speech, and the whole dataset was 30 h in total. In addition, we built two baseline systems based on state-of-the-art neural architectures, including a multi-speaker FastSpeech 2 model with HiFi-GAN vocoder and a full end-to-end VITS model for multi-speakers. On the system of FastSpeech2+HiFi-GAN, the three speakers scored 4.0 or higher on both naturalness evaluation and speaker similarity. In addition, the three speakers achieved scores of 4.5 or higher on the VITS model for naturalness evaluation and speaker similarity scores. The experimental results show that the published MnITS2 dataset can be used to build robust Mongolian multi-speaker TTS models.  
M 2023 2  
native Mongolian speaker and received much attention from academia and industry upon its release. This also shows the necessity of continuing to collect and organize Mongolian speech synthesis datasets and opening the baseline model's source code.  
Motivated by this, this paper presents a multi-speaker dataset called MnTTS2, which extends to three speakers and increases the data duration to 10 h for each speaker. The textual content has also been further expanded and enriched in the domain. Similar to our MnTTS, the MnTTS2 dataset is freely available to academics and industry practitioners.  
To demonstrate the reliability of MnTTS2, we built two baseline models. First, we combined the state-of-the-art FastSpeech2 [10] model with the HiFi-GAN [14] vocoder to build a model. Second, we built a fully end-to-end TTS system based on the VITS [17] model. After that, we conducted listening experiments and reported the results of three evaluations. These include the Naturalness Mean Opinion Score (N-MOS) for the naturalness of speech, the Speaker Similarity Mean Opinion Score (SS-MOS) for speaker similarity, and a robustness analysis to detect model stability. The experimental results show that our system can achieve satisfactory performance on the MnTTS2, which indicates that the MnTTS2 corpus is practically usable and can be used to build a robust multispeaker TTS system.  
Our main contributions are as follows. (1) We developed a multi-speaker TTS dataset, termed MnTTS2, containing three speakers. The total audio duration is about 30 h. The transcribed text covers various domains, such as sports and culture, etc. (2) We use the state-of-the-art non-autoregressive FastSpeech2 and the fully end-to-end VITS [17] to build the baseline models and validate our MnTTS2. (3) The MnTTS2 dataset, source code, and pre-trained models will be publicly available to academics and industry practitioners.  
We also highlight some differences between this work and our conference version [18]: (1) We add a new baseline model based on the latest and most powerful fully end-to-end VITS system and report the training details; (2) we conduct more comparative study with the additional baseline; and (3) we further conduct robustness evaluation on the basis of naturalness and speaker similarity experiments to validate our corpus and model more comprehensively.  
The rest of the paper is organized as follows. Section 2 revisits the related works about the Mongolian TTS corpus. In Section 3, we introduce the details of MnTTS2, including the corpus structure and statistical information. Section 4 explains and discusses the experimental setup and experimental results. Section 5 concludes and summarizes the work.