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arXiv · 2604.01247

Combining Masked Language Modeling and Cross-Modal Contrastive Learning for Prosody-Aware TTS

Kirill Borodin·Vasiliy Kudryavtsev·Maxim Maslov·Nikita Vasiliev·Mikhail Gorodnichev·Grach Mkrtchian
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Abstract

We investigate multi-stage pretraining for prosody modeling in diffusion-based TTS. A speaker-conditioned dual-stream encoder is trained with masked language modeling followed by SigLIP-style cross-modal contrastive learning using mixed-phoneme batches, with an additional same-phoneme refinement stage studied separately. We evaluate intrinsic text-audio retrieval and downstream synthesis in Grad-TTS and a latent diffusion TTS system. The two-stage curriculum (MLM + mixed-phoneme contrastive learning) achieves the best overall synthesis quality in terms of intelligibility, speaker similarity, and perceptual measures. Although same-phoneme refinement improves prosodic retrieval, it reduces phoneme discrimination and degrades synthesis. These findings indicate that improvements in embedding-space metrics do not necessarily translate to better generative performance and highlight the need to balance phoneme discrimination and prosodic sensitivity in TTS pretraining.

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Kirill Borodin, Vasiliy Kudryavtsev, Maxim Maslov, Nikita Vasiliev, Mikhail Gorodnichev, Grach Mkrtchian. 2026-03-31. Combining Masked Language Modeling and Cross-Modal Contrastive Learning for Prosody-Aware TTS. https://arxiv.org/abs/2604.01247

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