Depression Symptom Identification Through Acoustic Speech Analysis: A Transfer Learning Approach

Abstract

In the field of mental health diagnostics, the acoustic characteristics of speech have been recognized as potent markers for the identification of depressive symptoms. This study harnesses the power of transfer learning (TL) to discern depression-related sentiments from speech. Acoustic features such as rhythm, pitch, and tone form the core of this analysis. The methodology unfolds in three distinct phases. Initially, a Multi-Layer Perceptron (MLP) network employing stochastic gradient descent is applied to the RAVDESS dataset, yielding an accuracy of 65%. This finding catalyzes the second phase, wherein a comprehensive hyperparameter optimization via grid search (GS) is conducted on the MLP Classifier. This step primarily focuses on detecting emotions commonly associated with depression, including neutrality, sadness, anger, fear, and disgust. The optimized MLP classifier indicates an improved accuracy of 71%. In the final phase, to enhance precision further, the same GS-based model, underpinned by TL principles, is applied to the TASS dataset. This application astonishingly achieves an accuracy of 99.80%, suggesting a high risk of depression. This comparative study establishes the proposed framework as a vanguard in the application of TL for depression prediction, showcasing a significant leap in accuracy over previous methodologies.