中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
  5. 智能交互
WavDepressionNet: Automatic Depression Level Prediction via Raw Speech Signals

文献类型：期刊论文

作者Niu, Mingyue1; Tao, Jianhua2,3; Li, Yongwei4; Qin, Yong5; Li, Ya6
刊名IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
出版日期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
DOI10.1109/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.
WOS关键词SEVERITY ; FREQUENCY ; FEATURES ; NETWORK
资助项目State Key Laboratory of Multimodal Artificial Intelligence Systems
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001178971100018
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构State Key Laboratory of Multimodal Artificial Intelligence Systems
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.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。