Speech Emotion Recognition Algorithm for School Bullying Detection Based on MFCC-PCA-SVM Classification

Yuhao Wang; Xinsheng Wang; Chenguang He

doi:10.1007/978-981-15-8411-4_143

Speech Emotion Recognition Algorithm for School Bullying Detection Based on MFCC-PCA-SVM Classification

Yuhao Wang
, Xinsheng Wang^*
, Chenguang He

^*Corresponding author for this work

School of Electronics and Information Engineering

Harbin Institute of Technology Weihai

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

1 Scopus citations

Abstract

School bullying is common in school life, which has a negative impact on students’ physical and mental health. Nowadays, the research on school bullying at home and abroad mostly relies on human resources. In this paper, a school bullying detection algorithm based on pattern recognition techniques is presented. The authors firstly collect and pre-process the emotional speech data, and extract the MFCC features. Then they reduce the dimension of features to 6 by the PCA algorithm. They design a two-level SVM model in series with linear kernel for classification. The algorithm proposed in this paper effectively achieves a high recognition performance. The accuracy of the algorithm reaches 86.59% and the F1-Measure reaches 87.36%.

Original language	English
Title of host publication	Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems
Editors	Qilian Liang, Wei Wang, Xin Liu, Zhenyu Na, Xiaoxia Li, Baoju Zhang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	1088-1093
Number of pages	6
ISBN (Print)	9789811584107
DOIs	https://doi.org/10.1007/978-981-15-8411-4_143
State	Published - 2021
Event	9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020 - Changbaishan, China Duration: 4 Jul 2020 → 5 Jul 2020

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	654 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020
Country/Territory	China
City	Changbaishan
Period	4/07/20 → 5/07/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

MFCC
PCA
Pattern recognition
SVM
School bullying
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10.1007/978-981-15-8411-4_143

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Wang, Y., Wang, X., & He, C. (2021). Speech Emotion Recognition Algorithm for School Bullying Detection Based on MFCC-PCA-SVM Classification. In Q. Liang, W. Wang, X. Liu, Z. Na, X. Li, & B. Zhang (Eds.), Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems (pp. 1088-1093). (Lecture Notes in Electrical Engineering; Vol. 654 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-15-8411-4_143

Wang, Yuhao ; Wang, Xinsheng ; He, Chenguang. / Speech Emotion Recognition Algorithm for School Bullying Detection Based on MFCC-PCA-SVM Classification. Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems. editor / Qilian Liang ; Wei Wang ; Xin Liu ; Zhenyu Na ; Xiaoxia Li ; Baoju Zhang. Springer Science and Business Media Deutschland GmbH, 2021. pp. 1088-1093 (Lecture Notes in Electrical Engineering).

@inproceedings{6a84520605b34d488af89c717703b0be,

title = "Speech Emotion Recognition Algorithm for School Bullying Detection Based on MFCC-PCA-SVM Classification",

abstract = "School bullying is common in school life, which has a negative impact on students{\textquoteright} physical and mental health. Nowadays, the research on school bullying at home and abroad mostly relies on human resources. In this paper, a school bullying detection algorithm based on pattern recognition techniques is presented. The authors firstly collect and pre-process the emotional speech data, and extract the MFCC features. Then they reduce the dimension of features to 6 by the PCA algorithm. They design a two-level SVM model in series with linear kernel for classification. The algorithm proposed in this paper effectively achieves a high recognition performance. The accuracy of the algorithm reaches 86.59\% and the F1-Measure reaches 87.36\%.",

keywords = "MFCC, PCA, Pattern recognition, SVM, School bullying, Wrapper",

author = "Yuhao Wang and Xinsheng Wang and Chenguang He",

note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020 ; Conference date: 04-07-2020 Through 05-07-2020",

year = "2021",

doi = "10.1007/978-981-15-8411-4\_143",

language = "英语",

isbn = "9789811584107",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "1088--1093",

editor = "Qilian Liang and Wei Wang and Xin Liu and Zhenyu Na and Xiaoxia Li and Baoju Zhang",

booktitle = "Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems",

address = "德国",

}

Wang, Y, Wang, X & He, C 2021, Speech Emotion Recognition Algorithm for School Bullying Detection Based on MFCC-PCA-SVM Classification. in Q Liang, W Wang, X Liu, Z Na, X Li & B Zhang (eds), Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol. 654 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 1088-1093, 9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020, Changbaishan, China, 4/07/20. https://doi.org/10.1007/978-981-15-8411-4_143

Speech Emotion Recognition Algorithm for School Bullying Detection Based on MFCC-PCA-SVM Classification. / Wang, Yuhao; Wang, Xinsheng ; He, Chenguang.
Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems. ed. / Qilian Liang; Wei Wang; Xin Liu; Zhenyu Na; Xiaoxia Li; Baoju Zhang. Springer Science and Business Media Deutschland GmbH, 2021. p. 1088-1093 (Lecture Notes in Electrical Engineering; Vol. 654 LNEE).

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

TY - GEN

T1 - Speech Emotion Recognition Algorithm for School Bullying Detection Based on MFCC-PCA-SVM Classification

AU - Wang, Yuhao

AU - Wang, Xinsheng

AU - He, Chenguang

PY - 2021

Y1 - 2021

N2 - School bullying is common in school life, which has a negative impact on students’ physical and mental health. Nowadays, the research on school bullying at home and abroad mostly relies on human resources. In this paper, a school bullying detection algorithm based on pattern recognition techniques is presented. The authors firstly collect and pre-process the emotional speech data, and extract the MFCC features. Then they reduce the dimension of features to 6 by the PCA algorithm. They design a two-level SVM model in series with linear kernel for classification. The algorithm proposed in this paper effectively achieves a high recognition performance. The accuracy of the algorithm reaches 86.59% and the F1-Measure reaches 87.36%.

AB - School bullying is common in school life, which has a negative impact on students’ physical and mental health. Nowadays, the research on school bullying at home and abroad mostly relies on human resources. In this paper, a school bullying detection algorithm based on pattern recognition techniques is presented. The authors firstly collect and pre-process the emotional speech data, and extract the MFCC features. Then they reduce the dimension of features to 6 by the PCA algorithm. They design a two-level SVM model in series with linear kernel for classification. The algorithm proposed in this paper effectively achieves a high recognition performance. The accuracy of the algorithm reaches 86.59% and the F1-Measure reaches 87.36%.

KW - MFCC

KW - PCA

KW - Pattern recognition

KW - SVM

KW - School bullying

KW - Wrapper

UR - https://www.scopus.com/pages/publications/85111383678

U2 - 10.1007/978-981-15-8411-4_143

DO - 10.1007/978-981-15-8411-4_143

M3 - 会议稿件

AN - SCOPUS:85111383678

SN - 9789811584107

T3 - Lecture Notes in Electrical Engineering

SP - 1088

EP - 1093

BT - Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems

A2 - Liang, Qilian

A2 - Wang, Wei

A2 - Liu, Xin

A2 - Na, Zhenyu

A2 - Li, Xiaoxia

A2 - Zhang, Baoju

PB - Springer Science and Business Media Deutschland GmbH

T2 - 9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020

Y2 - 4 July 2020 through 5 July 2020

ER -

Wang Y, Wang X , He C. Speech Emotion Recognition Algorithm for School Bullying Detection Based on MFCC-PCA-SVM Classification. In Liang Q, Wang W, Liu X, Na Z, Li X, Zhang B, editors, Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems. Springer Science and Business Media Deutschland GmbH. 2021. p. 1088-1093. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-15-8411-4_143

Speech Emotion Recognition Algorithm for School Bullying Detection Based on MFCC-PCA-SVM Classification

Abstract

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UN SDGs

Keywords

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