{'fraction_non_alphanumeric': 0.012012012012012012, 'fraction_numerical': 0.006756756756756757, 'mean_word_length': 5.031674208144796, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Vocal tract and voice source features for monitoring cognitive workload

Monitoring cognitive workload from speech signals has received a lot of 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. 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 92 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 in-between 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 lowest MCR of 33.92 ± 1.05 was achieved with a SVM classifier.