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Analysis and detection of cognitive load and frustration in drivers' speech

By Hynek Boril, Seyed Omid Sadjadi, Tristan Kleinschmidt and John H. L. Hansen


Non-driving related cognitive load and variations of emotional state may impact a driver’s capability to control a vehicle and introduces driving errors. Availability of reliable cognitive load and emotion detection in drivers would benefit the design of active safety systems and other intelligent in-vehicle interfaces. In this study, speech produced by 68 subjects while driving in urban areas is analyzed. A particular focus is on speech production differences in two secondary cognitive tasks, interactions\ud with a co-driver and calls to automated spoken dialog systems (SDS), and two emotional states during the SDS interactions - neutral/negative. A number of speech parameters are found to vary across the cognitive/emotion classes. Suitability of selected cepstral- and production-based features for automatic cognitive\ud task/emotion classification is investigated. A fusion of\ud GMM/SVM classifiers yields an accuracy of 94.3% in cognitive\ud task and 81.3% in emotion classification

Topics: 080109 Pattern Recognition and Data Mining, 080602 Computer-Human Interaction, 090609 Signal Processing, Cognitive Load, Emotions, Speech Production Variations, Automatic Classification, Pattern Recognition
Publisher: International Speech Communication Association
Year: 2010
OAI identifier: oai:eprints.qut.edu.au:33168

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