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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Institute of Advanced Engineering and Science (IAES)
2018
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| Subjects: | |
| 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 |
| Summary: | 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. |
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