Unleashing the Potential of Convolutional Neural Networks for Automated Depression Detection Using Audio Modality

The conventional method for diagnosing depression involves conducting clinical interviews, wherein mental health professionals scrutinize patients' responses to evaluate their psychological state. In the realm of healthcare, tasks that rely on speech often necessitate the derivation of acoustic features that are manually engineered, such as pitch, prosody, loudness, speech rate, and energy. These features are then integrated with machine learning algorithms. Recent developments in deep learning have demonstrated significant achievements in automated learning for signal processing duties, such as the analysis of images, videos, and audio. The present study introduces a new methodology that utilizes Convolutional Neural Networks (CNNs) to identify depression through the analysis of audio signals. By utilizing CNN-based models, we aim to uncover hidden patterns and signals in the audio data for enhanced depression detection accuracy and reliability.