Publication Details

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

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. Speech production under stress for machine learning: multimodal dataset of 79 cases and 8 signals. Scientific data, 2024, vol. 11, no. 1, p. 1-9. ISSN: 2052-4463.
Czech title
Tvorba řeči ve stresu pro strojové učení: multimodální dataset 79 mluvčích a 8 signálů
Type
journal article
Language
English
Authors
Pešán Jan, Ing. (DCGM)
JUŘÍK, V.
RŮŽIČKOVÁ, A.
SVOBODA, V.
Janoušek Oto, Ing., Ph.D. (UBMI)
NĚMCOVÁ, A.
BOJANOVSKÁ, H.
ALDABAGHOVÁ, J.
KYSLÍK, F.
VODIČKOVÁ, K.
SODOMOVÁ, A.
BARTYS, P.
Chudý Peter, doc. Ing., Ph.D., MBA (VZ AeroWorks)
Černocký Jan, prof. Dr. Ing. (DCGM)
URL
Keywords

speech, stress, machine learning

Abstract

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.

Published
2024
Pages
1–9
Journal
Scientific data, vol. 11, no. 1, ISSN 2052-4463
DOI
10.1038/s41597-024-03991-w
UT WoS
001353330000007
EID Scopus
2-s2.0-85209350842
BibTeX 
@article{BUT193434,
  author="PEŠÁN, J. and JUŘÍK, V. and RŮŽIČKOVÁ, A. and SVOBODA, V. and JANOUŠEK, O. and NĚMCOVÁ, A. and BOJANOVSKÁ, H. and ALDABAGHOVÁ, J. and KYSLÍK, F. and VODIČKOVÁ, K. and SODOMOVÁ, A. and BARTYS, P. and CHUDÝ, P. and ČERNOCKÝ, J.",
  title="Speech production under stress for machine learning: multimodal dataset of 79 cases and 8 signals",
  journal="Scientific data",
  year="2024",
  volume="11",
  number="1",
  pages="1--9",
  doi="10.1038/s41597-024-03991-w",
  issn="2052-4463",
  url="https://www.nature.com/articles/s41597-024-03991-w"
}
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