A non-uniform subband approach to speech-based cognitive load classification

Speech has recently been recognized as an attractive method for the measurement of cognitive load. Current speech-based cognitive load measurement systems utilize acoustic features derived from auditory-motivated frequency scales. This paper aims to investigate the distribution of speech information specific to cognitive load discrimination as a function of frequency. We found that this distribution is neither uniform nor very similar to the Mel auditory scale and based on our experiments, we propose a novel non-uniform filterbank for acoustic feature extraction to classify cognitive load. Experimental results showed that the use of the proposed filterbank provided a relative improvement of about 10%, compared with the classification accuracy of the traditional cognitive load classification system based on a Mel-scale filterbank.