Interpreting symptoms of cognitive load in speech input
UM '99 Proceedings of the seventh international conference on User modeling
An Adaptive User Interface Based On Personalized Learning
IEEE Intelligent Systems
Learning Mixtures of Gaussians
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
When do we interact multimodally?: cognitive load and multimodal communication patterns
Proceedings of the 6th international conference on Multimodal interfaces
Combining Cepstral and Prosodic Features in Language Identification
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Think before you talk: an empirical study of relationship between speech pauses and cognitive load
Proceedings of the 20th Australasian Conference on Computer-Human Interaction: Designing for Habitus and Habitat
Automated stress detection using keystroke and linguistic features: An exploratory study
International Journal of Human-Computer Studies
A non-uniform subband approach to speech-based cognitive load classification
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Review: Integrating cognitive load theory and concepts of human-computer interaction
Computers in Human Behavior
Investigation of spectral centroid features for cognitive load classification
Speech Communication
Formant frequencies under cognitive load: effects and classification
EURASIP Journal on Advances in Signal Processing - Special issue on emotion and mental state recognition from speech
USAB'11 Proceedings of the 7th conference on Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society: information Quality in e-Health
Multimodal behavior and interaction as indicators of cognitive load
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on highlights of the decade in interactive intelligent systems
International Journal of Technology and Human Interaction
Hi-index | 0.00 |
Cognitive load variations have been found to impact multimodal behaviour, in particular, features of spoken input. In this paper, we present a design and implementation of a user study aimed at soliciting natural speech at three different levels of cognitive load. Some of the speech data produced was then used to train a number of models to automatically detect cognitive load. We describe a classification approach, the cognitive load levels were detected and output as discrete level ranges. The final system achieved a 71.1% accuracy for 3 levels classification in a speaker-independent setting. The ability to detect and manage a user's cognitive load can help us to adapt intelligent interfaces that ensure optimal user performance