Classification of cognitive load using voice features: A preliminary investigation

Abstract

Cognitive load classification has seen a boost in popularity lately among the speech analysis community. A number of handmade feature based methods and purely machine learning based methods were presented in the last few years, all trained on a small number of established datasets. This paper presents results of several machine learning methods used on an original dataset of voice samples from a preliminary pilot study into effects of cognitive load. Basic arithmetic problems were presented to the participants with instructions to answer them verbally. Acoustic voice features were extracted from the recorded utterances and modelled using methods like Support Vector Machines and Neural Networks. The accuracies of classification are presented over several conditions for a binary classification task (low cognitive load vs. high cognitive load). The viability of the basic arithmetic task as a dataset for cognitive load classification is discussed. Lessons learned during the analysis are also discussed and present a basis for a stronger experiment design using basic arithmetic tasks in the future.