Glottal features for speech-based cognitive load classification

Abstract

Cognitive load measurement is important when designing adaptive interfaces that optimize the performance of users working on high mental load tasks. Recent research on automatic speech-based measurement system indicates that cognitive load information is more prominent in the frequency region below 1 kHz. This study investigates the effects of cognitive load on glottal parameters (open quotient, normalized amplitude quotient and speed quotient), and proposes a system employing these parameters as features for cognitive load classification. Analysis of the glottal parameter distributions suggests that an increase in cognitive load can be related to a more creaky voice quality. Additionally, three-class classification results show that score-level fusion of systems based on the glottal features and baseline features (MFCCs, pitch, intensity and shifted delta cepstra) improves the baseline accuracy from 79% to 84%.