Arabic Speech Analysis for Classification and Prediction of Mental Illness due to Depression Using Deep Learning

Abstract

Depression is a global prevalent ailment for possible mental illness or mental disorder globally. Recognizing depressed early signs is critical for evaluating and preventing mental illness. With the progress of machine learning, it is possible to make intelligent systems capable of detecting depressive symptoms using speech analysis. This study presents a hybrid model to identify and predict mental illness from Arabic speech analysis due to depression. The proposed hybrid model comprises convolutional neural network (CNN) and a support vector machine (SVM) to identify and predict mental disorders. Experiments are performed on the Arabic speech benchmark data set of 200 speeches. A total of 70% of data were reserved for training, while 30% of data were to test the proposed model. The hybrid model (CNN + SVM) attained a 90.0% and 91.60% accuracy rate to predict the depression from Arabic speech analysis for training and testing stages. To authenticate the results of a proposed hybrid model, recurrent neural network (RNN) and CNN are also applied to the same data set individually, and the results are compared with each other. The RNN achieved an 80.70% and 81.60% accuracy rate to predict depression while speaking in the training and testing stages. The CNN predicted the depression in the training and testing stages with 88.50% and 86.60% accuracy rates. Based on the analysis, the proposed hybrid model secured better prediction results than individual RNN and CNN models on the same data set. Furthermore, the suggested model had a lower FPR, FNR, and higher accuracy, AUC, sensitivity, and specificity rate than individual RNN, CNN model performance in predicting depression. Finally, the achieved findings will be helpful to classify depression while speaking Arabic/speech and will be beneficial for physicians, psychiatrists, and psychologists in the detection of depression.

Early Detection of Autism Spectrum Disorder (ASD) using Traditional Machine Learning Models

“…This is because the achieved findings can be used to identify key indicators of depression in spoken Arabic, such as speech patterns, intonation, and pauses. These indicators can then be used to identify individuals who may be suffering from depression and help physicians, psychiatrists, and psychologists provide more effective treatment as in [22]. The mental health issues, such as depression and anxiety, are becoming more common, and people are recognizing the need to prioritize their mental health as well as their physical health.…”

“…Reddit's post-dataset cleaning process is related to removing personal information, punctuation marks, and URLs. CNN +SVM [22] Intelligent system to detect depressive symptoms using speech analysis Basic Arabic Vocal Emotions Dataset (BAVED) 90 and 91.60…”

“…The dataset has been prepared from the audio format for sentiment analysis. RNN+CNN [22] Intelligent system to detect depressive symptoms using speech analysis…”

Retracted: Arabic Speech Analysis for Classification and Prediction of Mental Illness due to Depression Using Deep Learning

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”