Human Voice as a Measure of Mental Load Level

Abstract

Purpose: The aim of this study was to determine a reliable and efficient set of acoustic parameters of the human voice able to estimate individuals' mental load level. Implementing detection methods and real-time analysis of mental load is a major challenge for monitoring and enhancing human task performance, especially during high-risk activities (e.g., flying aircraft).

Method: The voices of 32 participants were recorded during a cognitive task featuring word list recall. The difficulty of the task was manipulated by varying the number of words in each list (i.e., between 1 and 7, corresponding to 7 mental load conditions). Evoked pupillary response, known to be a useful proxy of mental load, was recorded simultaneously with speech to attest variations in mental load level during the experimental task.

Results: Classic features (fundamental frequency, its standard deviation, number of periods) and original features (frequency modulation and short-term variation in digital amplitude length) of the acoustic signals were predictive of memory load condition. They varied significantly according to the number of words to recall, specifically beyond a threshold of 3-5 words to recall, that is, when memory performance started to decline.

Conclusions: Some acoustic parameters of the human voice could be an appropriate and efficient means for detecting mental load levels.