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New framework synthesizes controllable dysarthric speech for ASR augmentation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel framework for synthesizing dysarthric speech, which can be used to augment datasets for automatic speech recognition (ASR) systems. This method separates speaker identity from pathological articulation, allowing for greater control over the generated speech. By using prompt-derived timbre prefixes and learnable patient-specific pathology prefixes, the system can generate speech that preserves target speaker characteristics while accurately reflecting dysarthric patterns. Experiments demonstrate that this synthetic data can effectively supplement real dysarthric speech data for ASR training. AI

IMPACT This research could improve ASR systems for individuals with speech impairments by providing more diverse and controllable training data.

RANK_REASON The cluster contains an academic paper detailing a new method for speech synthesis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework synthesizes controllable dysarthric speech for ASR augmentation

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The cluster contains an academic paper detailing a new method for speech synthesis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Haoshen Wang, Xueli Zhong, Bingbing Lin, Jia Huang, Xingduo Pan, Shengxiang Liang, Nizhuan Wang, Wai Ting Siok ·

    Controllable Dysarthric Speech Synthesis with Patient-Specific Conditioning for Speaker-Diverse ASR Augmentation

    arXiv:2602.08696v3 Announce Type: replace-cross Abstract: Dysarthric speech recognition is limited by high speaker variability and scarce labeled data. Existing synthesis methods often couple speaker identity with dysarthric articulation, reducing control over generated speech. W…

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