Public defence in Acoustics and Speech Technology, M.Sc. Farhad Javanmardi

The title of the thesis: Automatic Classification of Voice Disorders and Phonation Types from Speech Signals

Thesis defender: Farhad Javanmardi
Opponent: Prof. Daryush Mehta, Massachusetts General Hospital, US
Custos: Prof. Paavo Alku, Aalto University School of Electrical Engineering, Department of Information and Communications Engineering 

Speech is more than just a means of communication—it carries valuable information about our emotions, age, and health. Voice disorders, which may result from laryngeal diseases or neurological conditions, can significantly affect a person’s quality of life by impairing communication. This study utilizes artificial intelligence (AI) techniques to develop systems to automatically detect and classify voice disorders, as well as phonation types from speech signals. The thesis designs multiple systems by combining various feature extraction methods and supervised machine learning (ML) and deep learning (DL) techniques. 

This research contributes to the growing field of speech technology and digital health by studying the automatic speech-based classification of voice disorders and phonation types. This technology has potential for real-world applications, including use in clinical settings for early diagnosis and remote patient monitoring.

Thesis available for public display 10 days prior to the defence at: https://aaltodoc.aalto.fi/doc_public/eonly/riiputus/

Doctoral theses in the School of Electrical Engineering: https://aaltodoc.aalto.fi/handle/123456789/53