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In this paper we present our multimedia corpus of real-world driving data (NUDrive), built with the primary objective of firming foundations for applying digital signal processing technologies in the vehicular environment. NUDrive is a content rich corpus composed of driving, speech, video, and physiological signals. So far, we have collected data from 250 drivers, who drove an instrumented vehicle...
In this paper we present our latest achievements in the continuous estimation of a driver's spontaneous irritation. Experiments are conducted with data from 20 drivers, recorded under real driving conditions. While driving, participants also interact with a speech dialogue system to retrieve and play music. A fusion method is proposed to integrate information on the driving environment, driver behavior,...
We regarded information retrieval as a graph search problem and proposed several novel dialog strategies that can recover from misrecognition through a spoken dialog that traverses the graph. To recover from misrecognition without seeking confirmation, our system kept multiple understanding hypotheses at each turn and searched for a globally optimal hypothesis in the graph whose nodes express understanding...
To improve speech recognition performance, a feature transformation based on discriminant analysis has been widely used to reduce redundant dimensions of features. Linear discriminant analysis (LDA) and heteroscedastic discriminant analysis (HDA) are often used for this purpose, and a generalization method for LDA and HDA called power LDA (PLDA) has been proposed. However, these methods may result...
Although, in recent years, significant developments have been made in road safety, traffic statistics indicate that we still need significant improvements in the field. Since traffic accidents usually reflect human factors, in this paper, we focus on clarifying the understanding of driver behaviors under hazardous scenarios. Brake pedal signals or driver speech, or both, are utilized to detect incidents...
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