Total: 1
Speech based cognitive load estimation is a new field of research. Due to this relative `lack of maturity', a single best approach to building cognitive load estimation systems has not been established yet. The primary aim of this submission is to report the performance of various basic utterance level classification frameworks developed using important elements of state-of-the-art speaker recognition systems. This may lead to a suitable basis for future cognitive load estimation systems. As a consequence of being a part of a challenge, it is expected that these frameworks will be compared to a much larger number of alternative approaches than what would otherwise be possible. In keeping with this focused aim, the GMM supervector approaches along with some variants are utilised. The systems outlined in this paper include a frame-level MFCC-GMM system along with utterance level GMM-supervector-SVM, GMM-ivector-SVM and GMM-JFA-SVM systems. The best combined system has an accuracy (UAR) of 66.6% as evaluated on the challenge development set and 63.7% as evaluated on the test set.