Voice source features for cognitive load classification

Previous work in speech-based cognitive load classification has shown that the glottal source contains important information for cognitive load discrimination. However, the reliability of glottal flow features depends on the accuracy of the glottal flow estimation, which is a non-trivial process. In this paper, we propose the use of acoustic voice source features extracted directly from the speech spectrum (or cepstrum) for cognitive load classification. We also propose pre and post-processing techniques to improve the estimation of the cepstral peak prominence (CPP). 3-class classification results on two databases showed CPP as a promising cognitive load classification feature that outperforms glottal flow features. Score-level fusion of the CPP-based classification system with a formant frequency-based system yielded a final improved accuracy of 62.7%, suggesting that CPP contains useful voice source information that complements the information captured by vocal tract features.