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Authors: Dara Pir 1 and Jarek Krajewski 2

Affiliations: 1 Guttman Community College, City University of New York, United States ; 2 University of Wuppertal and Rhenish University of Applied Science Cologne, Germany

Keyword(s): Automatic Sleepiness Recognition, Acoustic Group Features, Computational Paralinguistics, Human-Computer Interaction.

Abstract: This paper compares the discriminating powers of various acoustic group features for the task of automatic sleepiness recognition using three different classifiers: Voted Perceptron, Simple Logistic, and Random Forest. Interspeech 2011 Sleepiness Sub-Challenge’s “Sleepy Language Corpus” (SLC) is used to generate the 4368 acoustic features of the official baseline feature set. The feature space is divided into Low-Level Descriptor (LLD) partitions. We consider the resulting feature space in groups rather than individually. A group feature corresponds to a set of one or more LLD partitions. The relevance of various group features to sleepiness state is then evaluated using the mentioned classifiers. Employing larger feature sets has been shown to increase the classification accuracy in sleepiness classification. Our results, however, demonstrate that a much smaller subset of the baseline feature set outperforms the official Sub-Challenge baseline on the SLC test data.

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Paper citation in several formats:
Pir, D. and Krajewski, J. (2018). Relevant Acoustic Group Features for Automatic Sleepiness Recognition. In Proceedings of the 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE; ISBN 978-989-758-299-8; ISSN 2184-4984, SciTePress, pages 209-214. DOI: 10.5220/0006779802090214

@conference{ict4awe18,
author={Dara Pir. and Jarek Krajewski.},
title={Relevant Acoustic Group Features for Automatic Sleepiness Recognition},
booktitle={Proceedings of the 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE},
year={2018},
pages={209-214},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006779802090214},
isbn={978-989-758-299-8},
issn={2184-4984},
}

TY - CONF

JO - Proceedings of the 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE
TI - Relevant Acoustic Group Features for Automatic Sleepiness Recognition
SN - 978-989-758-299-8
IS - 2184-4984
AU - Pir, D.
AU - Krajewski, J.
PY - 2018
SP - 209
EP - 214
DO - 10.5220/0006779802090214
PB - SciTePress

- Science and Technology Publications, Lda.
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