WavDepressionNet: Automatic Depression Level Prediction via Raw Speech Signals

doi:10.1109/TAFFC.2023.3272553

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 智能交互

	WavDepressionNet: Automatic Depression Level Prediction via Raw Speech Signals
	Niu, Mingyue 1; Tao, Jianhua 2,3; Li, Yongwei 4; Qin, Yong 5; Li, Ya 6
刊名	IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
	2024
卷号	15 期号:1 页码:285-296
关键词	Assessment block depression level prediction representation block speech signals WavDepressionNet
ISSN号	1949-3045
DOI	10.1109/TAFFC.2023.3272553
通讯作者	Tao, Jianhua(jhtao@tsinghua.edu.cn)
英文摘要	Physiological reports have confirmed that there are differences in speech signals between depressed and healthy individuals. Therefore, as an application in the field of affective computing, automatic depression level prediction through speech signals has received the attention of researchers, which often estimate the depression severity of individuals by the Fourier or Mel spectrograms of speech signals. However, some studies on speech emotion recognition suggest that directly modeling the raw speech signal is more helpful for extracting emotion-related information. Inspired by this fact, we develop a WavDepressionNet to model raw speech signals for the improvement of prediction accuracy. In our method, a representation block is proposed to find a set of basis vectors to construct the optimal transformation space and generate the transformation result (named Depression Feature Map, DFM) of speech signal for facilitating the perception of depression cues. We further propose an assessment block, which cannot only use the designed spatiotemporal self-calibration mechanism to calibrate the DFM and highlight the useful elements, but also aggregates the calibrated DFM across various temporal ranges with the dilated convolution. Experimental results on the AVEC 2013 and AVEC 2014 depression databases demonstrate the effectiveness of our approach over previous works.
资助项目	State Key Laboratory of Multimodal Artificial Intelligence Systems
WOS关键词	SEVERITY ; FREQUENCY ; FEATURES ; NETWORK
WOS研究方向	Computer Science
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:001178971100018
资助机构	State Key Laboratory of Multimodal Artificial Intelligence Systems
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/58021]
专题	模式识别国家重点实验室_智能交互
通讯作者	Tao, Jianhua
作者单位	1.Yanshan Univ, Sch Informat Sci & Engn, Qinhuangdao 066004, Hebei, Peoples R China 2.Tsinghua Univ, Dept Automat, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100049, Peoples R China 5.Nankai Univ, Sch Comp Sci, Tianjin 300350, Peoples R China 6.Beijing Univ Posts & Telecommun, Sch Artificial Intelligence, Beijing 100876, Peoples R China
推荐引用方式 GB/T 7714	Niu, Mingyue,Tao, Jianhua,Li, Yongwei,et al. WavDepressionNet: Automatic Depression Level Prediction via Raw Speech Signals[J]. IEEE TRANSACTIONS ON AFFECTIVE COMPUTING,2024,15(1):285-296.
APA	Niu, Mingyue,Tao, Jianhua,Li, Yongwei,Qin, Yong,&Li, Ya.(2024).WavDepressionNet: Automatic Depression Level Prediction via Raw Speech Signals.IEEE TRANSACTIONS ON AFFECTIVE COMPUTING,15(1),285-296.
MLA	Niu, Mingyue,et al."WavDepressionNet: Automatic Depression Level Prediction via Raw Speech Signals".IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 15.1(2024):285-296.