• 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