{'fraction_non_alphanumeric': 0.03699284009546539, 'fraction_numerical': 0.00477326968973747, 'mean_word_length': 5.991666666666666, '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}}
Automatic Speech Feature Extraction for Cognitive Load Classification

Performance of attention-demanding tasks is challenged if motor (e.g., walking) and cognitive (e.g., talking) tasks are carried out simultaneously. These dual-task paradigms have received increasing interest in probing the attentional influence associated with impairments to these systems [1]. For example, gait instabilities in Alzheimer's patients has been suggested to result from impaired attentional faculties impacting balance control [2]. Parkinson's patients have demonstrated inappropriate prioritization during dual-tasking, potentially leading to a higher risk of falls [3]. Furthermore, the controversy surrounding cell phone conversations on driving performance [4] further motivates the need to measure and understand the influence of attentional load.