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Despite the promise of multimodal context, current audio-language models struggle to leverage clinical information for dysarthric speech recognition, even degrading performance in some cases.
Smaller LLMs can achieve competitive controllable text simplification, but only if the training data adequately reflects the desired control attribute, revealing a critical data dependency often overlooked in ATS research.