Certain ASR transcripts and speech synthesized from them outperform manual transcripts in Alzheimer's disease detection, suggesting ASR errors can serve as useful diagnostic cues.
Experimental setup For fine-tuning the ASR models, we employed the AdamW op- timizer with a learning rate of 1 × 10−5 and weight decay of 5 × 10−3
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Beyond Manual Transcripts: The Potential of Automated Speech Recognition Errors in Improving Alzheimer's Disease Detection
Certain ASR transcripts and speech synthesized from them outperform manual transcripts in Alzheimer's disease detection, suggesting ASR errors can serve as useful diagnostic cues.