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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.
Phase information proves crucial for understanding consonant intelligibility in noise, challenging traditional views on speech processing.
The SFF-based narrowband DoA estimator not only mitigates spatial aliasing but also outperforms leading broadband methods in challenging acoustic environments.
