Speech production under stress for machine learning: multimodal dataset of 79 cases and 8 signals

• Published in: Scientific data Vol. 11; no. 1; pp. 1221 - 9
• Main Authors: Pešán, Jan, Juřík, Vojtěch, Ružičková, Alexandra, Svoboda, Vojtěch, Janoušek, Oto, Němcová, Andrea, Bojanovská, Hana, Aldabaghová, Jasmína, Kyslík, Filip, Vodičková, Kateřina, Sodomová, Adéla, Bartys, Patrik, Chudý, Peter, Černocký, Jan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.11.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/477/2811; 639/705/1046; Auditory stimuli; Cardiac stress tests; Civil engineering; Cognitive load; Computer engineering; Computer science; Data Descriptor; Datasets; Decision making; Electrical engineering; Electrocardiography; Experiments; Heart; Humanities and Social Sciences; Humans; Information technology; Informed consent; Machine Learning; multidisciplinary; Physiology; Science; Science (multidisciplinary); Speech; Stress analysis; Stress, Psychological
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-024-03991-w

Early identification of cognitive or physical overload is critical in fields where human decision making matters when preventing threats to safety and property. Pilots, drivers, surgeons, and operators of nuclear plants are among those affected by this challenge, as acute stress can impair their cognition. In this context, the significance of paralinguistic automatic speech processing increases for early stress detection. The intensity, intonation, and cadence of an utterance are examples of paralinguistic traits that determine the meaning of a sentence and are often lost in the verbatim transcript. To address this issue, tools are being developed to recognize paralinguistic traits effectively. However, a data bottleneck still exists in the training of paralinguistic speech traits, and the lack of high-quality reference data for the training of artificial systems persists. Regarding this, we present an original empirical dataset collected using the BESST experimental protocol for capturing speech signals under induced stress. With this data, our aim is to promote the development of pre-emptive intervention systems based on stress estimation from speech.