Wu, C., Ng, R.W.M., Torralba, O.S. et al. (1 more author) (2017) Analysing acoustic model changes for active learning in automatic speech recognition. In: International Conference on Systems, Signals and Image Processing (IWSSIP). International Conference on Systems, Signals and Image Processing (IWSSIP), 22-24 May 2017, Poznań, Poland. IEEE ISBN 978-1-5090-6344-4
Abstract
In active learning for Automatic Speech Recognition (ASR), a portion of data is automatically selected for manual transcription. The objective is to improve ASR performance with retrained acoustic models. The standard approaches are based on confidence of individual sentences. In this study, we look into an alternative view on transcript label quality, in which Gaussian Supervector Distance (GSD) is used as a criterion for data selection. GSD is a metric which quantifies how the model was changed during its adaptation. By using an automatic speech recognition transcript derived from an out-of-domain acoustic model, unsupervised adaptation was conducted and GSD was computed. The adapted model is then applied to an audio book transcription task. It is found that GSD provide hints for predicting data transcription quality. A preliminary attempt in active learning proves the effectiveness of GSD selection criterion over random selection, shedding light on its prospective use.
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: | Active learning; data selection; confidence measures; speaker adaptation |
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: | 10 Jul 2017 09:48 |
Last Modified: | 19 Dec 2022 13:36 |
Published Version: | https://doi.org/10.1109/IWSSIP.2017.7965609 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/IWSSIP.2017.7965609 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:118314 |