On the Use of Bhattacharyya based GMM Distance and Neural Net Features for Identification of Cognitive Load Levels

This paper presents a method for detecting cognitive load levels from speech. When speech is modulated by different lev-els of cognitive load, acoustic characteristics of speech change. In this paper, we measure acoustic distance of a stressed ut-terance from the baseline stress free speech using GMM-SVM kernel with Bhattacharyya based GMM distance. In addition, it is believed that airflow structure of speech production is non-linear. This motivates us to investigate better techniques to cap-ture nonlinear characteristic of stress information in acoustic features. Inspired by the recent success of neural networks for representation learning, we employ a single hidden layer feed forward network with non-linear activation to extract the fea-ture vectors. Furthermore, people have different reactions to a particular task load. This inter-speaker difference in stress re-sponses presents a major challenge for stress level detection. We use a bootstrapped training process to learn the stress re-sponse of a particular speaker. We perform experiments using data sets from Cognitive Load with Speech and EGG (CLSE) provided for the Cognitive Load Sub-Challenge of the INTER-SPEECH 2014 Computational Paralinguistics Challenge. The results show that the system with our proposed strategies per-forms well on validation and test sets. Index Terms: cognitive load, GMM-supervector, neural net feature