Driver behavior state recognition based on silence removal speech
Numerous researches have linked driver behavior to the cause of accident and some studies are concentrated into different input providing practical preventive measures. Nonetheless speech has been found to be a suitable input source in understanding and analyzing driver’s behavior state due to the...
| Main Authors: | , , , |
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| Format: | Proceeding Paper |
| Language: | English English |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2017
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| Online Access: | http://irep.iium.edu.my/57261/ http://irep.iium.edu.my/57261/1/52761_Driver%20behavior%20state_complete.pdf http://irep.iium.edu.my/57261/2/57261_Driver%20behavior%20state_SCOPUS_new.pdf |
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| author | Kamaruddin, Norhaslinda Abdul Rahman, Abdul Wahab Mohamad Halim, Khairul Ikhwan Mohd Noh, Muhammad Hafiq Iqma |
| author_facet | Kamaruddin, Norhaslinda Abdul Rahman, Abdul Wahab Mohamad Halim, Khairul Ikhwan Mohd Noh, Muhammad Hafiq Iqma |
| author_sort | Kamaruddin, Norhaslinda |
| building | IIUM Repository |
| collection | Online Access |
| description | Numerous researches have linked driver behavior
to the cause of accident and some studies are concentrated into different input providing practical preventive measures. Nonetheless speech has been found to be a suitable input source in understanding and analyzing driver’s behavior state due to the underlying emotional information when the driver speaks and such changes can be measured. 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) prior to extracting the relevant features in order to reduce the computational time in a vehicular environment. 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 is able to obtain comparable performance with accuracy ranging between 58.7% and 76.6% to differentiate four driver behavior states, namely; talking through cell telephone phone, out-burst laughing, sleepy and normal driving. It is envisages that such engine can be extended for a
more comprehensive driver behavior identification system that may acts as an embedded warning system for sleepy driver. |
| first_indexed | 2025-11-14T16:44:33Z |
| format | Proceeding Paper |
| id | iium-57261 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T16:44:33Z |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-572612017-06-13T05:39:48Z http://irep.iium.edu.my/57261/ Driver behavior state recognition based on silence removal speech Kamaruddin, Norhaslinda Abdul Rahman, Abdul Wahab Mohamad Halim, Khairul Ikhwan Mohd Noh, Muhammad Hafiq Iqma TJ Mechanical engineering and machinery Numerous researches have linked driver behavior to the cause of accident and some studies are concentrated into different input providing practical preventive measures. Nonetheless speech has been found to be a suitable input source in understanding and analyzing driver’s behavior state due to the underlying emotional information when the driver speaks and such changes can be measured. 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) prior to extracting the relevant features in order to reduce the computational time in a vehicular environment. 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 is able to obtain comparable performance with accuracy ranging between 58.7% and 76.6% to differentiate four driver behavior states, namely; talking through cell telephone phone, out-burst laughing, sleepy and normal driving. It is envisages that such engine can be extended for a more comprehensive driver behavior identification system that may acts as an embedded warning system for sleepy driver. Institute of Electrical and Electronics Engineers Inc. 2017-04-19 Proceeding Paper NonPeerReviewed application/pdf en http://irep.iium.edu.my/57261/1/52761_Driver%20behavior%20state_complete.pdf application/pdf en http://irep.iium.edu.my/57261/2/57261_Driver%20behavior%20state_SCOPUS_new.pdf Kamaruddin, Norhaslinda and Abdul Rahman, Abdul Wahab and Mohamad Halim, Khairul Ikhwan and Mohd Noh, Muhammad Hafiq Iqma (2017) Driver behavior state recognition based on silence removal speech. In: 1st International Conference on Informatics and Computing, ICIC 2016, 28-29 October, 2016, Mataram; Indonesi. http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7905713 10.1109/IAC.2016.7905713 |
| spellingShingle | TJ Mechanical engineering and machinery Kamaruddin, Norhaslinda Abdul Rahman, Abdul Wahab Mohamad Halim, Khairul Ikhwan Mohd Noh, Muhammad Hafiq Iqma Driver behavior state recognition based on silence removal speech |
| title | Driver behavior state recognition based on silence removal speech |
| title_full | Driver behavior state recognition based on silence removal speech |
| title_fullStr | Driver behavior state recognition based on silence removal speech |
| title_full_unstemmed | Driver behavior state recognition based on silence removal speech |
| title_short | Driver behavior state recognition based on silence removal speech |
| title_sort | driver behavior state recognition based on silence removal speech |
| topic | TJ Mechanical engineering and machinery |
| url | http://irep.iium.edu.my/57261/ http://irep.iium.edu.my/57261/ http://irep.iium.edu.my/57261/ http://irep.iium.edu.my/57261/1/52761_Driver%20behavior%20state_complete.pdf http://irep.iium.edu.my/57261/2/57261_Driver%20behavior%20state_SCOPUS_new.pdf |