Depression Detection in Arabic Using Speech Language Recognition

Abstract

Depression is one of the most common mental illnesses. Inaccurate assessments and misdiagnosis of illness are quite common for such mental disorder. In response to the issue of inaccurate assessment and misdiagnosis of depression, this study discusses the use of speech-language recognition to improve the detection of depression in Arabic speech. In this study, we extract speech features after collecting the dataset. These speech features can be obtained from both linguistic (uttered words) and para-linguistic (acoustic cues) features which we focus on. We classify the participants into two groups: clinically depressed and non-depressed. To do that, we start by recording speeches from interviews with the two groups. Then we extract para-linguistic features by using MFCC to help in building a model to detect depression. We use CNN to build the classification model. The accuracy of the classification model is 98% which will help in detecting depression depending on audio data.