With the progress of intelligent computers, the auto speech recognition function has developed rapidly. However, at present, the automatic recognition efficiency of on-board speech interaction is low and the recognition rate is poor. Even some vehicles do not display speech characters, which makes it impossible to verify whether the speech recognition is accurate. This paper proposes a multi-dimensional data method based on voice, picture data, text data, ADB data and internal changes of vehicle and machine, and improves the accuracy of vehicle borne voice recognition verification through multi-mode information fusion technology. Then, based on the parameters of speech wake-up rate recognition, false wake-up rate recognition, average wake-up time recognition, speech command response time recognition and speech command function recognition, a speech performance evaluation index model is established. Finally, the experimental results show that the verification method of multi-dimensional data speech recognition can verify the speech recognition rate and the execution rate of the vehicle in an all-round way.
After years of research on traffic accidents, it has been proved that serious traffic accidents will be caused by drivers' poor state, inattention, fatigue and other conditions. Computer vision obtains the driver's body information and face information through visual recognition technology, compares and analyzes with a large number of face database information, judges the driver's state, and makes corresponding reminders and warnings, so as to achieve driving safety. In this paper, the infrared camera is used for real-time face recognition and key point detection, and a multi feature fatigue driving detection method is proposed. Combined with OpenCV, the eye, mouth and head spatial posture coordinate points of the human face are located, and the fatigue is determined according to the change degree of blinking, yawning and nodding. Finally, the fusion algorithm is used to synthesize the above fatigue characteristic factors for fatigue prediction. Experiments show that the accuracy of the algorithm is more than 96%, and it has good stability and anti-interference ability.
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