@article{oai:nagoya.repo.nii.ac.jp:00007746,
 author = {Ariga, Michiaki and Yano, Yoshikazu and Doki, Shinji and Okuma, Shigeru},
 journal = {IEEE International Conference on Systems, Man and Cybernetics},
 month = {},
 note = {The focus of this paper is mental tension detection in speech to assist control the tension in day-to-day business such as conferences and operations in a call center. It is difficult to use classical techniques for mental tension detection in day-to-day business because those techniques require invasion body by electrodes or squirts and tied up by cables. In order to achieve a non-invasive, non-contact and low-restricting method, this proposed technique uses acoustic features in the speech. The technique uses the vocal tract model which represents the shape and the tightness of throat muscle. The Gaussian Mixture Model (GMM) classifies two mental tension states: high-tension and non-tension. The experiment result shows high recognition rate of mental tension detection.},
 pages = {2022--2027},
 title = {Mental Tension Detection in the Speech based on physiological monitoring},
 year = {2007}
}