Driver behaviour state recognition based on speech

Researches have linked the cause of traffic accident to driver behavior and some studies provided practical preventive measures based on different input sources. Due to its simplicity to collect, speech can be used as one of the input. The emotion information gathered from speech can be used to m...

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Main Authors:	Kamaruddin, Norhaslinda, Abdul Rahman, Abdul Wahab, Halim, Khairul Ikhwan Mohamad, Mohd Noh, Muhammad Hafiq Iqmal
Format:	Article
Language:	English English
Published:	Institute of Advanced Engineering and Science (IAES) 2018
Subjects:	HE Transportation and Communications TL1 Motor vehicles
Online Access:	http://irep.iium.edu.my/64206/ http://irep.iium.edu.my/64206/ http://irep.iium.edu.my/64206/ http://irep.iium.edu.my/64206/1/64206_Driver%20Behaviour%20State%20Recognition%20based%20on%20Speech_article.pdf http://irep.iium.edu.my/64206/2/64206_Driver%20Behaviour%20State%20Recognition%20based%20on%20Speech_scopus.pdf

id	iium-64206
recordtype	eprints
spelling	iium-642062018-06-28T03:54:30Z http://irep.iium.edu.my/64206/ Driver behaviour state recognition based on speech Kamaruddin, Norhaslinda Abdul Rahman, Abdul Wahab Halim, Khairul Ikhwan Mohamad Mohd Noh, Muhammad Hafiq Iqmal HE Transportation and Communications TL1 Motor vehicles Researches have linked the cause of traffic accident to driver behavior and some studies provided practical preventive measures based on different input sources. Due to its simplicity to collect, speech can be used as one of the input. The emotion information gathered from speech can be used to measure driver behavior state based on the hypothesis that emotion influences driver behavior. However, the massive amount of driving speech data may hinder optimal performance of processing and analyzing the data due to the computational complexity and time constraint. This paper presents a silence removal approach using Short Term Energy (STE) and Zero Crossing Rate (ZCR) in the pre-processing phase to reduce the unnecessary processing. Mel Frequency Cepstral Coefficient (MFCC) feature extraction method coupled with Multi-Layer Perceptron (MLP) classifier are employed to get the driver behavior state recognition performance. Experimental results demonstrated that the proposed approach can obtain comparable performance with accuracy ranging between 58.7% and 76.6% to differentiate four driver behavior states, namely; talking through mobile phone, laughing, sleepy and normal driving. It is envisaged that such approach can be extended for a more comprehensive driver behavior identification system that may acts as an embedded warning system for sleepy driver. Institute of Advanced Engineering and Science (IAES) 2018-04-01 Article PeerReviewed application/pdf en http://irep.iium.edu.my/64206/1/64206_Driver%20Behaviour%20State%20Recognition%20based%20on%20Speech_article.pdf application/pdf en http://irep.iium.edu.my/64206/2/64206_Driver%20Behaviour%20State%20Recognition%20based%20on%20Speech_scopus.pdf Kamaruddin, Norhaslinda and Abdul Rahman, Abdul Wahab and Halim, Khairul Ikhwan Mohamad and Mohd Noh, Muhammad Hafiq Iqmal (2018) Driver behaviour state recognition based on speech. Telkomnika, 16 (2). pp. 852-861. ISSN 1693-6930 E-ISSN 2087-278X http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/8416/pdf_666 10.12928/TELKOMNIKA.v16i2.8416
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	HE Transportation and Communications TL1 Motor vehicles
spellingShingle	HE Transportation and Communications TL1 Motor vehicles Kamaruddin, Norhaslinda Abdul Rahman, Abdul Wahab Halim, Khairul Ikhwan Mohamad Mohd Noh, Muhammad Hafiq Iqmal Driver behaviour state recognition based on speech
description	Researches have linked the cause of traffic accident to driver behavior and some studies provided practical preventive measures based on different input sources. Due to its simplicity to collect, speech can be used as one of the input. The emotion information gathered from speech can be used to measure driver behavior state based on the hypothesis that emotion influences driver behavior. However, the massive amount of driving speech data may hinder optimal performance of processing and analyzing the data due to the computational complexity and time constraint. This paper presents a silence removal approach using Short Term Energy (STE) and Zero Crossing Rate (ZCR) in the pre-processing phase to reduce the unnecessary processing. Mel Frequency Cepstral Coefficient (MFCC) feature extraction method coupled with Multi-Layer Perceptron (MLP) classifier are employed to get the driver behavior state recognition performance. Experimental results demonstrated that the proposed approach can obtain comparable performance with accuracy ranging between 58.7% and 76.6% to differentiate four driver behavior states, namely; talking through mobile phone, laughing, sleepy and normal driving. It is envisaged that such approach can be extended for a more comprehensive driver behavior identification system that may acts as an embedded warning system for sleepy driver.
format	Article
author	Kamaruddin, Norhaslinda Abdul Rahman, Abdul Wahab Halim, Khairul Ikhwan Mohamad Mohd Noh, Muhammad Hafiq Iqmal
author_facet	Kamaruddin, Norhaslinda Abdul Rahman, Abdul Wahab Halim, Khairul Ikhwan Mohamad Mohd Noh, Muhammad Hafiq Iqmal
author_sort	Kamaruddin, Norhaslinda
title	Driver behaviour state recognition based on speech
title_short	Driver behaviour state recognition based on speech
title_full	Driver behaviour state recognition based on speech
title_fullStr	Driver behaviour state recognition based on speech
title_full_unstemmed	Driver behaviour state recognition based on speech
title_sort	driver behaviour state recognition based on speech
publisher	Institute of Advanced Engineering and Science (IAES)
publishDate	2018
url	http://irep.iium.edu.my/64206/ http://irep.iium.edu.my/64206/ http://irep.iium.edu.my/64206/ http://irep.iium.edu.my/64206/1/64206_Driver%20Behaviour%20State%20Recognition%20based%20on%20Speech_article.pdf http://irep.iium.edu.my/64206/2/64206_Driver%20Behaviour%20State%20Recognition%20based%20on%20Speech_scopus.pdf
first_indexed	2023-09-18T21:31:04Z
last_indexed	2023-09-18T21:31:04Z
_version_	1777412514419572736

Driver behaviour state recognition based on speech

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