Ng, W., Nicolao, M. and Hain, T. (2017) Unsupervised crosslingual adaptation of tokenisers for spoken language recognition. Computer Speech and Language, 46. pp. 327-342. ISSN 0885-2308
Abstract
Phone tokenisers are used in spoken language recognition (SLR) to obtain elementary phonetic information. We present a study on the use of deep neural network tokenisers. Unsupervised crosslingual adaptation was performed to adapt the baseline tokeniser trained on English conversational telephone speech data to different languages. Two training and adaptation approaches, namely cross-entropy adaptation and state-level minimum Bayes risk adaptation, were tested in a bottleneck i-vector and a phonotactic SLR system. The SLR systems using the tokenisers adapted to different languages were combined using score fusion, giving 7-18% reduction in minimum detection cost function (minDCF) compared with the baseline configurations without adapted tokenisers. Analysis of results showed that the ensemble tokenisers gave diverse representation of phonemes, thus bringing complementary effects when SLR systems with different tokenisers were combined. SLR performance was also shown to be related to the quality of the adapted tokenisers.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | ©2017 Published by Elsevier Ltd. This is an open access article article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Language recognition; unsupervised adaptation; crosslingual adaptation; tokenisation; phonotactic SLR |
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) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC) UNSPECIFIED GOOGLE NONE |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 May 2017 14:03 |
Last Modified: | 07 Nov 2018 09:50 |
Published Version: | https://doi.org/10.1016/j.csl.2017.05.002 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.csl.2017.05.002 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:116618 |