MUGAHED AL EZZI

Early detection of depression allows rapid intervention and reduce the escalation of the disorder. Conventional method requires patient to seek diagnosis and treatment by visiting a trained clinician. Bio-sensors technology such as automatic depression detection using speech can be used to assist early diagnosis for detecting remotely those who are at risk. In this research, we focus on detecting depression using Bahasa Malaysia language using speech signals that are recorded remotely via subject’s personal mobile devices. Speech recordings from a total of 43 depressed subjects and 47 healthy subjects were gathered via online platform with diagnosis validation according to the Malay beck depression inventory II (Malay BDI-II), patient health questionnaire (PHQ-9) and subject’s declaration of major depressive disorder (MDD) diagnosis by a trained clinician. Classifier models were compared using time-based and spectrum-based microphone independent feature set with hyperparameter tunin...

One of the most challenging techniques for speech analysis applications in mobile phones is acoustic feature extraction. The adverse environment noises, diversity of microphone specifications, and various recording software have a significant effect on the values of the extracted acoustic features. In this study, we investigate the robustness of different types of acoustic features related to time-based, frequency-based, and sustained vowel using 11 different mobile recording devices. 49 recordings of subjects reciting the Rainbow Passage and 25 recordings of sustained vowel /a/ were collected. By way of synchronous recording, we analyzed and compared the extracted 253-dimensional acoustic feature vectors in order to examine how consistent the data values between the different recording devices. The variability of data values was measured using the method of coefficient of variance. Data values with low variability were identified to be from features such as the transition parameter...