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arXiv · 2506.16127

Improved Intelligibility of Dysarthric Speech using Conditional Flow Matching

Shoutrik Das·Nishant Singh·Arjun Gangwar·S Umesh
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Abstract

Dysarthria is a neurological disorder that significantly impairs speech intelligibility, often rendering affected individuals unable to communicate effectively. This necessitates the development of robust dysarthric-to-regular speech conversion techniques. In this work, we investigate the utility and limitations of self-supervised learning (SSL) features and their quantized representations as an alternative to mel-spectrograms for speech generation. Additionally, we explore methods to mitigate speaker variability by generating clean speech in a single-speaker voice using features extracted from WavLM. To this end, we propose a fully non-autoregressive approach that leverages Conditional Flow Matching (CFM) with Diffusion Transformers to learn a direct mapping from dysarthric to clean speech. Our findings highlight the effectiveness of discrete acoustic units in improving intelligibility while achieving faster convergence compared to traditional mel-spectrogram-based approaches.

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Shoutrik Das, Nishant Singh, Arjun Gangwar, S Umesh. 2025-06-19. Improved Intelligibility of Dysarthric Speech using Conditional Flow Matching. https://arxiv.org/abs/2506.16127

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