Ragni, A. orcid.org/0000-0003-0634-4456, Saunders, D., Zahemszky, P. et al. (3 more authors) (2017) Morph-to-word transduction for accurate and efficient automatic speech recognition and keyword search. 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, LA, USA. IEEE , pp. 5770-5774. ISBN 9781509041183
Abstract
Word units are a popular choice in statistical language modelling. For inflective and agglutinative languages this choice may result in a high out of vocabulary rate. Subword units, such as morphs, provide an interesting alternative to words. These units can be derived in an unsupervised fashion and empirically show lower out of vocabulary rates. This paper proposes a morph-to-word transduction to convert morph sequences into word sequences. This enables powerful word language models to be applied. In addition, it is expected that techniques such as pruning, confusion network decoding, keyword search and many others may benefit from word rather than morph level decision making. However, word or morph systems alone may not achieve optimal performance in tasks such as keyword search so a combination is typically employed. This paper proposes a single index approach that enables word, morph and phone searches to be performed over a single morph index. Experiments are conducted on IARPA Babel program languages including the surprise languages of the OpenKWS 2015 and 2016 competitions.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. |
Keywords: | morph-to-word transduction; speech recognition; keyword search; single index |
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: | 15 Nov 2019 10:09 |
Last Modified: | 15 Nov 2019 10:09 |
Published Version: | https://ieeexplore.ieee.org/abstract/document/7953... |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/icassp.2017.7953262 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:152807 |