• Gitanjali Tukaram Gadakh
• Kavita Sawant
• Pranoti Kale
• Snehal Avhad
• Pooja Shinde

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.

Machine Learning, Face detection, Image preprocessing, segmentation, extraction, CNN, SVM, MFCC, Depression detection

"Depression detection using Face, Text and Audio", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 11, page no.d433-d439, November-2022, Available :http://www.jetir.org/papers/JETIR2211358.pdf

"Depression detection using Face, Text and Audio", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 11, page no. ppd433-d439, November-2022, Available at : http://www.jetir.org/papers/JETIR2211358.pdf