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An SVM-based physical fatigue diagnostic model using speech features
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  • Journal title : Phonetics and Speech Sciences
  • Volume 8, Issue 2,  2016, pp.17-22
  • Publisher : The Korean Society of Speech Sciences
  • DOI : 10.13064/KSSS.2016.8.2.017
 Title & Authors
An SVM-based physical fatigue diagnostic model using speech features
Kim, Tae Hun; Kwon, Chul Hong;
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 Abstract
This paper devises a model to diagnose physical fatigue using speech features. This paper presents a machine learning method through an SVM algorithm using the various feature parameters. The parameters used include the significant speech parameters, questionnaire responses, and bio-signal parameters obtained before and after the experiment imposing the fatigue. The results showed that performance rates of 95%, 100%, and 90%, respectively, were observed from the proposed model using three types of the parameters relevant to the fatigue. These results suggest that the method proposed in this study can be used as the physical fatigue diagnostic model, and that fatigue can be easily diagnosed by speech technology.
 Keywords
physical fatigue;diagnosis;speech features;SVM;
 Language
Korean
 Cited by
 References
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