Detail výsledku
BESST Dataset: A Multimodal Resource for Speech-based Stress Detection and Analysis
JUŘÍK, V.
Karafiát Martin, Ing., Ph.D., UPGM (FIT)
Černocký Jan, prof. Dr. Ing., UPGM (FIT)
The Brno Extended Stress and Speech Test (BESST) dataset
is a new resource for the speech research community, offering
multimodal audiovisual, physiological and psychological data
that enable investigations into the interplay between stress and
speech. In this paper, we introduce the BESST dataset and provide a details of its design, collection protocols, and technical
aspects. The dataset comprises speech samples, physiologi-
cal signals (including electrocardiogram, electrodermal activity,
skin temperature, and acceleration data), and video recordings
from 90 subjects performing stress-inducing tasks. It comprises
16.9 hours of clean Czech speech data, averaging 15 minutes of
clean speech per participant. The data collection procedure involves the induction of cognitive and physical stress induced by
Reading Span task (RSPAN) and Hand Immersion (HIT) task
respectively. The BESST dataset was collected under stringent
ethical standards and is accessible for research and development.
BESST dataset, stress recognition, multimodal data, speech research, physiological signals, cognitive load, speech production
@inproceedings{BUT193740,
author="PEŠÁN, J. and JUŘÍK, V. and KARAFIÁT, M. and ČERNOCKÝ, J.",
title="BESST Dataset: A Multimodal Resource for Speech-based Stress Detection and Analysis",
booktitle="Proceedings of Interspeech 2024",
year="2024",
journal="Proceedings of Interspeech",
volume="2024",
number="9",
pages="1355--1359",
publisher="International Speech Communication Association",
address="Kos",
doi="10.21437/Interspeech.2024-42",
issn="1990-9772",
url="https://www.isca-archive.org/interspeech_2024/pesan24_interspeech.pdf"
}Ústav počítačové grafiky a multimédií (UPGM)