Bell, P., Gales, M., Hain, T. orcid.org/0000-0003-0939-3464 et al. (8 more authors) (2015) The MGB Challenge: Evaluating Multi-genre Broadcast Media Recognition. In: Proceedings of the 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU). 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), 13-17 Dec 2015, Scottsdale, AZ. IEEE , pp. 687-693. ISBN 978-1-4799-7291-3
Abstract
This paper describes the Multi-Genre Broadcast (MGB) Challenge at ASRU 2015, an evaluation focused on speech recognition, speaker diarization, and "lightly supervised" alignment of BBC TV recordings. The challenge training data covered the whole range of seven weeks BBC TV output across four channels, resulting in about 1,600 hours of broadcast audio. In addition several hundred million words of BBC subtitle text was provided for language modelling. A novel aspect of the evaluation was the exploration of speech recognition and speaker diarization in a longitudinal setting - i.e. recognition of several episodes of the same show, and speaker diarization across these episodes, linking speakers. The longitudinal tasks also offered the opportunity for systems to make use of supplied metadata including show title, genre tag, and date/time of transmission. This paper describes the task data and evaluation process used in the MGB challenge, and summarises the results obtained.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2015 IEEE. This is an author produced version of a paper subsequently published in 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU). Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC) UNSPECIFIED |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Aug 2016 09:31 |
Last Modified: | 19 Dec 2022 13:34 |
Published Version: | http://dx.doi.org/10.1109/ASRU.2015.7404863 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ASRU.2015.7404863 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:101807 |