Investigation of spectral centroid features for cognitive load classification
Speech is a promising modality for the convenient measurement of cognitive load, and recent years have seen the development of several cognitive load classification systems. Many of these systems have utilised mel frequency cepstral coefficients (MFCC) and prosodic features like pitch and intensity to discriminate between different cognitive load levels. However, the accuracies obtained by these systems are still not high enough to allow for their use outside of laboratory environments. One reason for this might be the imperfect acoustic description of speech provided by MFCCs. Since these features do not characterise the distribution of the spectral energy within subbands, in this paper, we investigate the use of spectral centroid frequency (SCF) and spectral centroid amplitude (SCA) features, applying them to the problem of automatic cognitive load classification. The effect of varying the number of filters and the frequency scale used is also evaluated, in terms of the effectiveness of the resultant spectral centroid features in discriminating between cognitive loads. The results of classification experiments show that the spectral centroid features consistently and significantly outperform a baseline system employing MFCC, pitch, and intensity features. Experimental results reported in this paper indicate that the fusion of an SCF based system with an SCA based system results in a relative reduction in error rate of 39% and 29% for two different cognitive load databases.
spectral centroid feature cognitive load frequency scale gaussian mixture model kullbackleibler distance