Audio Frequency-Time Dual Domain Evaluation on Depression Diagnosis
This addresses the problem of complex and ambiguous diagnostic procedures for depression, offering a potential tool for assessment and screening, though it appears incremental as it builds on existing voice-based methods.
The study tackled depression diagnosis by using voice as a physiological signal with frequency-time dual domain multimodal characteristics and deep learning models, achieving excellent performance in classification tasks.
Depression, as a typical mental disorder, has become a prevalent issue significantly impacting public health. However, the prevention and treatment of depression still face multiple challenges, including complex diagnostic procedures, ambiguous criteria, and low consultation rates, which severely hinder timely assessment and intervention. To address these issues, this study adopts voice as a physiological signal and leverages its frequency-time dual domain multimodal characteristics along with deep learning models to develop an intelligent assessment and diagnostic algorithm for depression. Experimental results demonstrate that the proposed method achieves excellent performance in the classification task for depression diagnosis, offering new insights and approaches for the assessment, screening, and diagnosis of depression.