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

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

Článek WoS

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.

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.

speech, stress, machine learning

speech, stress, machine learning

PEŠÁN, J.; JUŘÍK, V.; RŮŽIČKOVÁ, A.; SVOBODA, V.; JANOUŠEK, O.; NĚMCOVÁ, A.; BOJANOVSKÁ, H.; ALDABAGHOVÁ, J.; KYSLÍK, F.; VODIČKOVÁ, K.; SODOMOVÁ, A.; BARTYS, P.; CHUDÝ, P.; ČERNOCKÝ, J.

2025

12.11.2024

Springer Nature

2052-4463

Scientific Data

11

1

Spojené království Velké Británie a Severního Irska

1

9

9

https://www.nature.com/articles/s41597-024-03991-w

http://hdl.handle.net/11012/250852

pesan_sci data_2024_s41597-024-03991-w