  While deep learning-based text-to-speech (TTS) models such as VITS have shown
excellent results, they typically require a sizable set of high-quality <text,
audio> pairs to train, which is expensive to collect. So far, most languages in
the world still lack the training data needed to develop TTS systems. This
paper proposes two improvement methods for the two problems faced by
low-resource Mongolian speech synthesis: a) In view of the lack of high-quality
<text, audio> pairs of data, it is difficult to model the mapping problem from
linguistic features to acoustic features. Improvements are made using
pre-trained VITS model and transfer learning methods. b) In view of the problem
of less labeled information, this paper proposes to use an automatic prosodic
annotation method to label the prosodic information of text and corresponding
speech, thereby improving the naturalness and intelligibility of low-resource
Mongolian language. Through empirical research, the N-MOS of the method
proposed in this paper is 4.195, and the I-MOS is 4.228.
