Classification of Cognitive Load Using Voice Features: A Preliminary Investigation (CROSBI ID 652614)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Mijić, Igor ; Šarlija, Marko ; Petrinović, Davor
engleski
Classification of Cognitive Load Using Voice Features: A Preliminary Investigation
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 modeled 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.
cognitive load, basic arithmetic, classification, machine learning
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Podaci o prilogu
345-350.
2017.
objavljeno
Podaci o matičnoj publikaciji
Institute of Electrical and Electronics Engineers (IEEE)
978-1-5386-1264-4
Podaci o skupu
8th IEEE International Conference on Cognitive Infocommunications - CogInfoCom 2017
predavanje
11.09.2017-14.09.2017
Debrecen, Mađarska