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arXiv · 2506.00350

DiffDSR: Dysarthric Speech Reconstruction Using Latent Diffusion Model

Xueyuan Chen·Dongchao Yang·Wenxuan Wu·Minglin Wu·Jing Xu·Xixin Wu·Zhiyong Wu·Helen Meng
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Abstract

Dysarthric speech reconstruction (DSR) aims to convert dysarthric speech into comprehensible speech while maintaining the speaker's identity. Despite significant advancements, existing methods often struggle with low speech intelligibility and poor speaker similarity. In this study, we introduce a novel diffusion-based DSR system that leverages a latent diffusion model to enhance the quality of speech reconstruction. Our model comprises: (i) a speech content encoder for phoneme embedding restoration via pre-trained self-supervised learning (SSL) speech foundation models; (ii) a speaker identity encoder for speaker-aware identity preservation by in-context learning mechanism; (iii) a diffusion-based speech generator to reconstruct the speech based on the restored phoneme embedding and preserved speaker identity. Through evaluations on the widely-used UASpeech corpus, our proposed model shows notable enhancements in speech intelligibility and speaker similarity.

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Xueyuan Chen, Dongchao Yang, Wenxuan Wu, Minglin Wu, Jing Xu, Xixin Wu, Zhiyong Wu, Helen Meng. 2025-05-31. DiffDSR: Dysarthric Speech Reconstruction Using Latent Diffusion Model. https://arxiv.org/abs/2506.00350

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