Voice source under cognitive load: Effects and classification

Abstract

Cognitive load refers to the mental demand experienced while performing a cognitive task. A cognitive load measurement system can potentially be a useful tool for monitoring and enhancing human task performance. In the area of speech-based cognitive load classification, while there are various spectral and vocal tract-based features proposed for classification purposes, there is still a lack of studies that investigate how cognitive load affects the voice source, and whether glottal features are effective in cognitive load classification systems. This work introduces a set of databases that contains both speech and electroglottograph (EGG) data. Using these databases, we present results that provide arguably the first direct insight into how cognitive load affects the voice source. Additionally, we show that glottal-based features carry complementary information with respect to formant-based features, and that fusion between glottal and formant-based systems produces classification results that are comparable with (if not better than) existing baseline systems across three out of five evaluation databases.