DocumentCode
3714503
Title
Feature selection and classification of speech under long-term stress
Author
Bin Hu; Zhenyu Liu; Lihua Yan; Tianyang Wang; Fei Liu; Xiaoyu Li; Huanyu Kang
Author_Institution
Ubiquitous Awareness and Intelligent Solutions Lab, Lanzhou University, China
fYear
2015
Firstpage
904
Lastpage
910
Abstract
Many studies were proposed to discuss acoustic correlates of stress in recent years. Considering some inconsistent experiment results, we supposed that stress should be categorized into long-term and short-term stress in this topic, and the trend of short-term stress induced by workload may be affected by long-term stress. This study contains three parts: first, we proposed an acoustic feature set chosen by feature selection, which can be considered as a measurement of the level of long-term stress; second, we showed that this set is immune to short-term stress in stress classification tests; finally, we observed some particular voice features mentioned in previous researches in our experiment and the results may imply that short-term stress trend is in connection with the level of long-term stress. In short, long-term and shot-term stress should be discussed separately in future researches for clear and explicit conclusions.
Keywords
"Support vector machines","Niobium","Atmospheric measurements","Particle measurements","Jitter","Education","Market research"
Publisher
ieee
Conference_Titel
Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on
Type
conf
DOI
10.1109/BIBM.2015.7359804
Filename
7359804
Link To Document