Depression is a common and serious mood disorder that negatively affects the patient's capacity of functioning normally in daily tasks. Speech is proven to be a vigorous tool in depression diagnosis. Research in psychiatry concentrated on performing fine-grained analysis on word-level speech components contributing to the manifestation of depression in speech and revealed significant variations at the phoneme-level in depressed speech. On the other hand, research in Machine Learning-based automatic recognition of depression from speech focused on the exploration of various acoustic features for the detection of depression and its severity level. Few have focused on incorporating phoneme-level speech components in automatic assessment systems. In this paper, we propose an Artificial Intelligence (AI) based application for clinical depression recognition and assessment from speech. We investigate the acoustic characteristics of phoneme units, specifically vowels and consonants for depression recognition via Deep Learning. We present and compare three spectrogram-based Deep Neural Network architectures, trained on phoneme consonant and vowel units and their fusion respectively. Our experiments show that the deep learned consonant-based acoustic characteristics lead to better recognition results than vowel-based ones. The fusion of vowel and consonant speech characteristics through a deep network significantly outperforms the single space networks as well as the state-of-art deep learning approaches on the DAIC-WOZ database.
RBA-FE: A Robust Brain-Inspired Audio Feature Extractor for Depression Diagnosis
School of Future Technology, South China University of Technology（华南理工大学未来技术学院）；Pazhou Lab, Guangzhou（广州琶洲实验室）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）
Towards Robust Deep Neural Networks for Affect and Depression Recognition from Speech
University of Paris-Est Créteil（巴黎东-克雷泰伊大学）；Sorbonne University（索邦大学）；INSAT（国立应用科学技术学院）；ENSIIE（国立工业环境高等工程师学院）；Polytechnic University of Hauts-de-France（上法兰西理工大学）
Deep Learning for Depression Recognition with Audiovisual Cues: A Review
Xi’an University of Posts and Telecommunications（西安邮电大学）；University of Chinese Academy of Sciences（中国科学院大学）；Aalto University（阿尔托大学）；Northwest University（西北大学）；Northwestern Polytechnical University（西北工业大学）
Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences（中国科学院生物医学成像科学与系统重点实验室）
