Current audio-language models fail to use clinical multimodal context for dysarthric speech recognition, but context-aware LoRA fine-tuning delivers large accuracy gains on the SAP dataset.
Huang and Kenneth Watkin and Simone Frame , year =
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Phonological subspace collapse in SSL speech representations produces aetiology-specific degradation profiles that remain stable in shape across languages and model architectures.
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When Audio-Language Models Fail to Leverage Multimodal Context for Dysarthric Speech Recognition
Current audio-language models fail to use clinical multimodal context for dysarthric speech recognition, but context-aware LoRA fine-tuning delivers large accuracy gains on the SAP dataset.
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Phonological Subspace Collapse Is Aetiology-Specific and Cross-Lingually Stable: Evidence from 3,374 Speakers
Phonological subspace collapse in SSL speech representations produces aetiology-specific degradation profiles that remain stable in shape across languages and model architectures.