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
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
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Speech has been recognized as an attractive method for the measurement of cognitive load. Previous approaches have used mel frequency cepstral coefficients (MFCCs) as discriminative features to classify cognitive load. The MFCCs contain information from both the voice source and the vocal tract, so that the individual contributions of each to cognitive load variation are unclear. This paper aims to extract speech features related to either the voice source or the vocal tract and use them to discriminate between cognitive load levels in order to identify the individual contribution of each for cognitive load measurement. Voice source-related features are then used to improve the performance of current cognitive load classification systems, using adapted Gaussian mixture models. Our experimental result shows that the use of voice source feature could yield around 12% reduction in relative error rate compared with the baseline system based on MFCCs, intensity, and pitch contour.