Audio Frequency-Time Dual Domain Evaluation on Depression Diagnosis

2025-10-25

AI Summary

Depression screening faces challenges including diagnostic complexity, ambiguous clinical criteria, and low help-seeking rates. To address these, this paper proposes a voice-based multimodal intelligent diagnostic approach that jointly models temporal and spectral features of speech signals—marking the first effort to integrate both domains for depression assessment and overcoming the limited representational capacity of conventional single-domain analyses. We design a deep neural network architecture enabling end-to-end time-frequency joint representation learning, augmented by feature optimization and discriminative classification strategies. Evaluated on a publicly available depressive speech dataset, our method achieves 92.3% classification accuracy, significantly outperforming single-domain baseline models. This work establishes a novel, non-invasive, and scalable paradigm for automated depression screening, demonstrating strong potential for clinical decision support and large-scale community-level mental health surveillance.

Technology Category

• Natural Language Processing: Speech
• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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.

Problem

• Developing intelligent depression diagnosis using voice signals
• Leveraging frequency-time dual domain multimodal characteristics
• Addressing complex diagnostic procedures and ambiguous criteria

Innovation

• Uses voice as physiological signal for diagnosis
• Leverages frequency-time dual domain multimodal features
• Applies deep learning models for depression classification