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Cleaner speech datasets may boost in-domain performance but can undermine generalization, revealing a hidden cost in Alzheimer's detection models.
Emotion-sensitive neurons in multimodal models reveal shared mechanisms for recognizing emotions across speech and faces, with implications for enhancing emotion recognition capabilities.
Depression detection models may be learning *who* is speaking, not *how* depression manifests in speech, inflating reported accuracy.