Feature selection and classification of speech under long-term stress

Bin Hu; Zhenyu Liu; Lihua Yan; Tianyang Wang; Fei Liu; Xiaoyu Li; Huanyu Kang

doi:10.1109/BIBM.2015.7359804

Feature selection and classification of speech under long-term stress

Bin Hu^*, Zhenyu Liu, Lihua Yan, Tianyang Wang, Fei Liu, Xiaoyu Li, Huanyu Kang

^*Corresponding author for this work

Lanzhou University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Editors	lng. Matthieu Schapranow, Jiayu Zhou, Xiaohua Tony Hu, Bin Ma, Sanguthevar Rajasekaran, Satoru Miyano, Illhoi Yoo, Brian Pierce, Amarda Shehu, Vijay K. Gombar, Brian Chen, Vinay Pai, Jun Huan
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	904-910
Number of pages	7
ISBN (Electronic)	9781467367981
DOIs	https://doi.org/10.1109/BIBM.2015.7359804
Publication status	Published - 16 Dec 2015
Externally published	Yes
Event	IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 - Washington, United States Duration: 9 Nov 2015 → 12 Nov 2015

Publication series

Name	Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

Conference

Conference	IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Country/Territory	United States
City	Washington
Period	9/11/15 → 12/11/15

Keywords

acoustic feature
classification
feature selection
stress

Access to Document

10.1109/BIBM.2015.7359804

Cite this

Hu, B., Liu, Z., Yan, L., Wang, T., Liu, F., Li, X., & Kang, H. (2015). Feature selection and classification of speech under long-term stress. In L. M. Schapranow, J. Zhou, X. T. Hu, B. Ma, S. Rajasekaran, S. Miyano, I. Yoo, B. Pierce, A. Shehu, V. K. Gombar, B. Chen, V. Pai, & J. Huan (Eds.), Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 (pp. 904-910). Article 7359804 (Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2015.7359804

Hu, Bin ; Liu, Zhenyu ; Yan, Lihua et al. / Feature selection and classification of speech under long-term stress. Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015. editor / lng. Matthieu Schapranow ; Jiayu Zhou ; Xiaohua Tony Hu ; Bin Ma ; Sanguthevar Rajasekaran ; Satoru Miyano ; Illhoi Yoo ; Brian Pierce ; Amarda Shehu ; Vijay K. Gombar ; Brian Chen ; Vinay Pai ; Jun Huan. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 904-910 (Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015).

@inproceedings{d8fd63b47d93446db3441ab4d0bcd45c,

title = "Feature selection and classification of speech under long-term stress",

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 = "acoustic feature, classification, feature selection, stress",

author = "Bin Hu and Zhenyu Liu and Lihua Yan and Tianyang Wang and Fei Liu and Xiaoyu Li and Huanyu Kang",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 ; Conference date: 09-11-2015 Through 12-11-2015",

year = "2015",

month = dec,

day = "16",

doi = "10.1109/BIBM.2015.7359804",

language = "English",

series = "Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "904--910",

editor = "Schapranow, {lng. Matthieu} and Jiayu Zhou and Hu, {Xiaohua Tony} and Bin Ma and Sanguthevar Rajasekaran and Satoru Miyano and Illhoi Yoo and Brian Pierce and Amarda Shehu and Gombar, {Vijay K.} and Brian Chen and Vinay Pai and Jun Huan",

booktitle = "Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015",

address = "United States",

}

Hu, B, Liu, Z, Yan, L, Wang, T, Liu, F, Li, X & Kang, H 2015, Feature selection and classification of speech under long-term stress. in LM Schapranow, J Zhou, XT Hu, B Ma, S Rajasekaran, S Miyano, I Yoo, B Pierce, A Shehu, VK Gombar, B Chen, V Pai & J Huan (eds), Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015., 7359804, Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015, Institute of Electrical and Electronics Engineers Inc., pp. 904-910, IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015, Washington, United States, 9/11/15. https://doi.org/10.1109/BIBM.2015.7359804

Feature selection and classification of speech under long-term stress. / Hu, Bin; Liu, Zhenyu; Yan, Lihua et al.
Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015. ed. / lng. Matthieu Schapranow; Jiayu Zhou; Xiaohua Tony Hu; Bin Ma; Sanguthevar Rajasekaran; Satoru Miyano; Illhoi Yoo; Brian Pierce; Amarda Shehu; Vijay K. Gombar; Brian Chen; Vinay Pai; Jun Huan. Institute of Electrical and Electronics Engineers Inc., 2015. p. 904-910 7359804 (Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Feature selection and classification of speech under long-term stress

AU - Hu, Bin

AU - Liu, Zhenyu

AU - Yan, Lihua

AU - Wang, Tianyang

AU - Liu, Fei

AU - Li, Xiaoyu

AU - Kang, Huanyu

PY - 2015/12/16

Y1 - 2015/12/16

N2 - 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.

AB - 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.

KW - acoustic feature

KW - classification

KW - feature selection

KW - stress

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U2 - 10.1109/BIBM.2015.7359804

DO - 10.1109/BIBM.2015.7359804

M3 - Conference contribution

AN - SCOPUS:84962339232

T3 - Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

SP - 904

EP - 910

BT - Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

A2 - Schapranow, lng. Matthieu

A2 - Zhou, Jiayu

A2 - Hu, Xiaohua Tony

A2 - Ma, Bin

A2 - Rajasekaran, Sanguthevar

A2 - Miyano, Satoru

A2 - Yoo, Illhoi

A2 - Pierce, Brian

A2 - Shehu, Amarda

A2 - Gombar, Vijay K.

A2 - Chen, Brian

A2 - Pai, Vinay

A2 - Huan, Jun

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

Y2 - 9 November 2015 through 12 November 2015

ER -

Hu B, Liu Z, Yan L, Wang T, Liu F, Li X et al. Feature selection and classification of speech under long-term stress. In Schapranow LM, Zhou J, Hu XT, Ma B, Rajasekaran S, Miyano S, Yoo I, Pierce B, Shehu A, Gombar VK, Chen B, Pai V, Huan J, editors, Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 904-910. 7359804. (Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015). doi: 10.1109/BIBM.2015.7359804

Feature selection and classification of speech under long-term stress

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Keywords

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