Siamese Neural Network for Speech-Based Depression Classification and Severity Assessment

Abstract

The evaluation of an individual’s mental health and behavioral functioning, known as psychological assessment, is generally conducted by a mental health professional. This process aids in diagnosing mental health conditions, identifying suitable treatment options, and assessing progress during treatment. Currently, national health systems are unable to cope with the constantly growing demand for such services. To address and expedite the diagnosis process, this study suggests an AI-powered tool capable of delivering understandable predictions through the automated processing of the captured speech signals. To this end, we employed a Siamese neural network (SNN) elaborating on standardized speech representations free of domain expert knowledge. Such an SNN-based framework is able to address multiple downstream tasks using the same latent representation. Interestingly, it has been applied both for classifying speech depression as well as assessing its severity. After extensive experiments on a publicly available dataset following a standardized protocol, it is shown to significantly outperform the state of the art with respect to both tasks. Last but not least, the present solution offers interpretable predictions, while being able to meaningfully interact with the medical experts.