The INTERSPEECH 2018 computational paralinguistics challenge : atypical & self-assessed affect, crying & heart beats |
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Author: | Schuller, Björn W.1,2,3; Steidl, Stefan4; Batliner, Anton2,4; |
Organizations: |
1GLAM – Group on Language, Audio & Music, Imperial College London, UK 2ZD.B Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Germany 3audEERING GmbH, Gilching, Germany
4Pattern Recognition Lab, FAU Erlangen-Nuremberg, Germany
5iDN – interdisciplinary Developmental Neuroscience, Medical University of Graz, Austria 6University Medical Center Göttingen, Germany 7Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden 8Department of Clinical Psychology and Psychotherapy, University of Ulm, Germany 9Shenzhen University General Hospital, Shenzhen, P.R. China 10Machine Intelligence & Signal Processing Group, Technische Universität München, Germany 11University of Oulu, Finland |
Format: | article |
Version: | published version |
Access: | open |
Online Access: | PDF Full Text (PDF, 0.3 MB) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe202003037022 |
Language: | English |
Published: |
International Speech Communication Association,
2018
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Publish Date: | 2020-03-03 |
Description: |
AbstractThe INTERSPEECH 2018 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Atypical Affect Sub-Challenge, four basic emotions annotated in the speech of handicapped subjects have to be classified; in the Self-Assessed Affect Sub-Challenge, valence scores given by the speakers themselves are used for a three-class classification problem; in the Crying Sub-Challenge, three types of infant vocalisations have to be told apart; and in the Heart Beats Sub-Challenge, three different types of heart beats have to be determined. We describe the Sub-Challenges, their conditions and baseline feature extraction and classifiers, which include data-learnt (supervised) feature representations by end-to-end learning, the ‘usual’ ComParE and BoAW features and deep unsupervised representation learning using the AUDEEP toolkit for the first time in the challenge series. see all
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Series: |
Interspeech |
ISSN: | 1990-9772 |
ISSN-L: | 1990-9772 |
ISBN Print: | 978-1-5108-7221-9 |
Pages: | 122 - 126 |
DOI: | 10.21437/Interspeech.2018-51 |
OADOI: | https://oadoi.org/10.21437/Interspeech.2018-51 |
Host publication: |
19th Annual Conference of the International Speech Communication, INTERSPEECH 2018 |
Host publication editor: |
Sekhar, C.C. Rao, P. Ghosh, P.K. Murthy, H.A. Yegnanarayana, B. Umesh, S. Alku, P. Prasanna, S.R.M. Narayanan, S. |
Conference: |
Annual Conference of the International Speech Communication |
Type of Publication: |
A4 Article in conference proceedings |
Field of Science: |
213 Electronic, automation and communications engineering, electronics |
Subjects: | |
Copyright information: |
© The Authors 2018. |