Driving Fatigue Detection Method Based on Speech Personality Features and Model Adaptation

Abstract

The invention provides a driving fatigue detection method based on voice personality characteristics and models. The method comprises the following steps of: firstly, extracting linear characteristics and non-linear characteristics of driver voice samples; secondly, utilizing a speaker identification algorithm based on VQ to judge the identity of a driver; then, according to the individual fatigue characteristic differences of the driver, utilizing a Relief algorithm to screen out voice characteristics fully reflecting fatigue information thereof, and constructing fatigue personality characteristic vectors; and finally, adopting an SVM classification algorithm to establish a self-adaptive fatigue detection model of the driver himself, and carrying out sample training and driving fatigue detection on the model. According to the invention, the voice linear characteristics and non-linear characteristics are combined in a complementation manner, and the voice personality characteristics fully reflecting fatigue information of the driver are screened out from the individual differences of the driver for driving fatigue detection, the influences of the individual voice differences of the driver on fatigue detection are effectively reduced, and the detection accuracy is improved.

Description

technical field [0001] The invention relates to the fields of voice processing and traffic safety monitoring and control, in particular to a method for detecting driving fatigue by applying voice personality characteristics and model self-adaptation. Background technique [0002] In the field of transportation, driver fatigue directly endangers the safety of life and property. It is reported that about 80% of major traffic accidents are related to the driver's fatigue driving. The phenomenon of driving fatigue and the traffic safety hazards caused by it have attracted great attention from the society, and the research on its detection methods has always been a hot topic of concern. question. [0003] At present, there are mainly two methods for the detection of driving fatigue, subjective and objective. The subjective detection method is mainly based on subjective questionnaires, self-record sheets, sleep record sheets, etc. to evaluate the degree of fatigue of the human bo...

AI Technical Summary

Problems solved by technology

In recent years, research on the application of speech signals to detect human fatigue has also gradually emerged. However, most of the research is limited to some traditional speech linear feature parameters, or limited to nonlinear characteristics such as chaos and fractal of speech signals. The fatigue information contained is not comprehensive enough: on the one hand, if more features are obtained, the amount of calculation will increase sharply, and even the problem of low detection efficiency caused by the "curse of dimensionality"; at the same time, speech features that are susceptible to interference from other factors It will also affect the accuracy and objectivity of fatigue detection; on the other hand, it is difficult to ensure the comprehensiveness and universality of fatigue detection due to fewer speech features

More importantly, the individual pronunciation differences of the speakers have a great influence on the effect of fatigue detection, especially the sensitivity of each voice feature of different speakers to their fatigue state is different, if the same driver is used for all drivers The combination of speech features and the same fatigue detection model are bound to be unreasonable and objective

Therefore, the existing similar methods are not ideal in terms of the differences in fatigue characteristics of different individuals and the adaptability of the fatigue detection model, and the detection effect is also far from the actual application.

Images