GIST Scientists Advance Voice Pathology Detection via Adversarial Continual Learning
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Tuesday, October 17, 2023
Diagnosis, IEEE Signal Processing Society, Partnership, Quality of life, Paralysis, Intelligence, Human, Overfitting, Principal investigator, CNN, UAR, Saarbrücken, GIST, Human voice, Dick's Picks Volume 30, Cancer, Vocal cords, VPD, MIT, Speech, Gwangju Institute of Science and Technology, Cyst, EECS, Contrastive, Pathology, SVM, Pi, Catherine Disher, Research, TAPT, Phonation, Mobile phone
In this context, voice pathology detection (VPD) has received much attention as a non-invasive way to automatically detect voice problems.
Key Points:
- In this context, voice pathology detection (VPD) has received much attention as a non-invasive way to automatically detect voice problems.
- It consists of two processing modules: a feature extraction module to characterize normal voices and a voice detection module to detect abnormal ones.
- Machine learning methods like support vector machines (SVM) and convolutional neural networks (CNN) have been successfully utilized as pathological voice detection modules to achieve good VPD performance.
- Herein, they incorporated adversarial regularization during the continual learning process.