Voice pathology detection and classification by adopting online sequential extreme learning machine

• Main Authors: Al-Dhief, F. T., Baki, M. M., Abdul Latiff, N. M., Malik, N. N. N. A., Salim, N. S., Albader, M. A. A., Mahyuddin, N. M., Mohammed, M. A.
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2021
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/95129/1/NurulMuazzahAbdulLatiff2021_VoicePathologyDetection.pdf; http://eprints.utm.my/id/eprint/95129/; http://dx.doi.org/10.1109/ACCESS.2021.3082565

In the last decade, the implementation of machine learning algorithms in the analysis of voice disorder is paramount in order to provide a non-invasive voice pathology detection by only using audio signal. In spite of that, most recent systems of voice pathology work on a limited acoustic database....