Milner, R., Saz, O., Deena, S. et al. (3 more authors) (2015) The 2015 Sheffield System for Longitudinal Diarisation of Broadcast Media. 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. 632-638. ISBN 978-1-4799-7291-3
Abstract
Speaker diarisation is the task of answering "who spoke when" within a multi-speaker audio recording. Diarisation of broadcast media typically operates on individual television shows, and is a particularly difficult task, due to a high number of speakers and challenging background conditions. Using prior knowledge, such as that from previous shows in a series, can improve performance. Longitudinal diarisation allows to use knowledge from previous audio files to improve performance, but requires finding matching speakers across consecutive files. This paper describes the University of Sheffield system for participation in the 2015 Multi-Genre Broadcast (MGB) challenge. The challenge required longitudinal diarisation of data from BBC archives, under very constrained resource settings. Our system consists of three main stages: speech activity detection using DNNs with novel adaptation and decoding methods; speaker segmentation and clustering, with adaptation of the DNN-based clustering models; and finally speaker linking to match speakers across shows. The final result on the development set of 19 shows from five different television series provided a Diarisation Error Rate of 50.77% in the diarisation and linking task.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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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: |
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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: | 16 Aug 2016 09:45 |
Last Modified: | 19 Dec 2022 13:34 |
Published Version: | http://dx.doi.org/10.1109/ASRU.2015.7404855 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ASRU.2015.7404855 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:101812 |