Depression Detection from Text, Image & Speech using Deep Learning Algorithm
Keywords:
Machine Learning, Face detection, Image preprocessing, segmentation, extraction, CNN, SVM, MFCC, Depression detectionAbstract
Depression is a serious illness that affects millions of people globally. From child to senior citizens are facing depression. Major area is occupied by adults, college going students and teenagers also. In recent years, the task of automatic depression detection from speech has gained popularity. However, several challenges remain, including which features provide the best discrimination between classes or depression levels. We provide a comparative analyses of various features for depression detection. Using the same corpus, we evaluate how a system built on text-based , audio- based and speech-based system. We find that a combination of features drawn from both speech and text lead to the best system performance. By doing a survey we have find most efficient algorithms for detection purpose. We have used CNN (Convolutional Neural Network) for Face images training, for Face recognition we have used Harr Cascade Algorithm. To detect depression using Text ,we have used SVM(Support Vector Machine) Algorithm. Lastly for Audio input, we have used MFCC for speech recognition.
