Publication Details
BESST Dataset: A Multimodal Resource for Speech-based Stress Detection and Analysis
JUŘÍK, V.
Karafiát Martin, Ing., Ph.D. (DCGM)
Černocký Jan, prof. Dr. Ing. (DCGM)
BESST dataset, stress recognition, multimodal data, speech research,
physiological signals, cognitive load, speech production
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.
@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"
}