Audio Analysis for Depressive State Detection Using Convolutional Neural Networks

Convolutional Neural Networks (CNNs) for speech analysis to detect depressive states are described. U sing Mel-Spectrograms, extracting relevant information from the input by analyzing audio properties including pitch, energy, and prosody. The CNN model performs better than conventional models such as Support Vector Machines (SVM), Random Forest, K Nearest Neighbor Algorithm (KNN), and Naive Bayes. Operational effectiveness indicators such as precision, Fl- Score, recall, and accuracy are computed and differentiated based on a dataset of three thousand audio samples. Research outcomes, execution costs, and programmatic implementations are emphasized.