Monitoring Cognitive Workload Using Vocal Tract and Voice Source Features

  • Eydis Huld Magnusdottir Reykjavik University
  • Michal Borsky Reykjavik University
  • Manuela Meier Technical University of Munich
  • Kamilla Johannsdottir Reykjavik University
  • Jon Gudnason Reykjavik University

Abstract

Monitoring cognitive workload from speech signals has received much attention from researchers in the past few years as it has the potential to improve performance and fidelity in human decision making. The bulk of the research has focused on classifying speech from talkers participating in cognitive workload experiments using simple reading tasks, memory span tests and the Stroop test, typically into three levels of low, medium and high cognitive workload. This study focuses on using parameters extracted from the vocal tract and the voice source components of the speech signal for cognitive workload monitoring. The experiment used in this study contains 98 participants, the levels were obtained by using a reading task and three Stroop tasks which were randomly ordered for each participant and an adequate rest time was used inbetween tasks to mitigate the effect of cognitive workload from one task affecting the subsequent one. Vocal tract features were obtained from the first three formants and voice source features were extracted using signal analysis on the inverse filtered speech signal. The results show that on their own, the vocal tract features outperform the voice source features. The MCR of 33.92% ± 1.05 was achieved with a SVM classifier. A weighted combination of vocal tract and voice source features classified with SWM classifier fused at the output level achieved the lowest MCR of  32.5%.

Keywords

speech science, voice source signal, vocal tract features, computational paralinguistic
Published
23-05-2017
How to Cite
MAGNUSDOTTIR, Eydis Huld et al. Monitoring Cognitive Workload Using Vocal Tract and Voice Source Features. Periodica Polytechnica Electrical Engineering and Computer Science, [S.l.], may 2017. ISSN 2064-5279. Available at: <https://pp.bme.hu/eecs/article/view/10414>. Date accessed: 26 sep. 2017. doi: https://doi.org/10.3311/PPee.10414.
Section
Articles