Self-supervised Prosody Learning at Phoneme-level with Momentum Contrast for Speech Synthesis

Abstract

This paper investigates leveraging large-scale speech data to enhance prosodic modeling in speech synthesis, and introduces a model named SP2MC which achieves self-supervised prosody learning at phoneme-level with momentum contrast. This model incorporates dual convolutional encoders for speech and linear predictive coding (LPC) residual inputs to generate phoneme-level embeddings, which are masked and processed by a Transformer model to produce prosody representations. Two supervision modules are employed to generate phoneme-level supervision from speech waveforms and residuals. Momentum contrast is utilized to manage negative sample selection in contrastive learning. Finally, the SP2MC representations are integrated into a Fastspeech2-based acoustic model for speech synthesis. Experimental results indicate that the naturalness of speech synthesized by the proposed method is significantly better than that of baselines.