Mokaram, S. and Moore, R.K. orcid.org/0000-0003-0065-3311 (2017) The Sheffield Search and Rescue corpus. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 05-09 Mar 2017, New Orleans, USA. IEEE , pp. 5840-5844. ISBN 9781509041176
Abstract
© 2017 IEEE. As part of an ongoing research into extracting mission-critical information from Search and Rescue speech communications, a corpus of unscripted, goal-oriented, two-party spoken conversations has been designed and collected. The Sheffield Search and Rescue (SSAR) corpus comprises about 12 hours of data from 96 conversations by 24 native speakers of British English with a southern accent. Each conversation is about a collaborative task of exploring and estimating a simulated indoor environment. The task has carefully been designed to have a quantitative measure for the amount of exchanged information about the discourse subject. SSAR includes different layers of annotations which should be of interest to researchers in a wide range of human/human conversation understanding as well as automatic speech recognition. It also provides an amount of data for analysis of multiple parallel conversations around a single subject. The SSAR corpus is available for research purposes.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Conversational speech corpus; goal-oriented conversation; spoken language understanding; automatic speech recognition |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 25 Aug 2017 10:52 |
Last Modified: | 19 Dec 2022 13:36 |
Published Version: | https://doi.org/10.1109/ICASSP.2017.7953276 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ICASSP.2017.7953276 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:120572 |