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University of Southern California
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Tailored acoustic feature selection can boost dysarthric speech recognition accuracy by over 4.6%, transforming how we approach ASR for low-resource groups.
Tailored acoustic features can boost dysarthric speech recognition performance by over 4% using advanced neural network models.
Tailored data augmentation techniques can reduce word error rates in dysarthric speech recognition by over 30%, depending on severity.
A multimodal approach that integrates audio and textual data achieves unprecedented accuracy in diagnosing respiratory diseases, outperforming traditional methods.
EEG foundation models may not be the automatic win you think they are: they shine on long-context tasks but falter in short-window and channel-constrained scenarios, where smaller supervised models can compete.
Widely used emotion embedding similarity metrics for speech generation are more sensitive to speaker and linguistic features than actual emotion, rendering them unreliable for evaluating emotional expressiveness.
