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首页> 外文会议>2016 International Conference on Informatics and Computing >Driver behavior state recognition based on silence removal speech
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Driver behavior state recognition based on silence removal speech

机译：基于静音消除语音的驾驶员行为状态识别

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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.
机译：许多研究已将驾驶员的行为与事故原因联系在一起，并且一些研究集中在不同的输入上，以提供实用的预防措施。然而，由于驾驶员讲话时潜在的情绪信息并且可以测量这种变化，因此已经发现语音是理解和分析驾驶员行为状态的合适输入源。但是，由于计算复杂性和时间限制，大量的驾驶语音数据可能会妨碍处理和分析数据的最佳性能。本文提出了一种在提取相关特征之前使用短期能量（STE）和过零率（ZCR）的静音消除方法，以减少车辆环境中的计算时间。采用梅尔频率倒谱系数（MFCC）特征提取方法与多层感知器（MLP）分类器相结合，获得驾驶员行为状态识别性能。实验结果表明，提出的方法能够获得可比的性能，其准确度介于58.7％和76.6％之间，以区分四种驾驶员行为状态。通过手机通话，爆发式笑声，困倦和正常驾驶。设想可以将这种引擎扩展为更全面的驾驶员行为识别系统，该系统可以充当困倦驾驶员的嵌入式警告系统。

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