Low-Resource Mongolian Speech Synthesis Based on Automatic Prosody Annotation

TL;DR

This paper enhances low-resource Mongolian speech synthesis by employing transfer learning with VITS and automatic prosody annotation, significantly improving naturalness and intelligibility despite limited data.

Contribution

It introduces a novel approach combining transfer learning and automatic prosody annotation to address data scarcity in low-resource Mongolian TTS systems.

Findings

N-MOS score of 4.195

I-MOS score of 4.228

Improved naturalness and intelligibility

Abstract

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.