Errattahi, R., El Hannani, A., Ouahmane, H. et al. (1 more author) (2016) Automatic Speech Recognition Errors Detection Using Supervised Learning Techniques. In: 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA). 13th International Conference of Computer Systems and Applications (AICCSA), Nov 29 – Dec 02, 2016, Agadir, Morocco. IEEE
Abstract
Over the last years, many advances have been made in the field of Automatic Speech Recognition (ASR). However, the persistent presence of ASR errors is limiting the widespread adoption of speech technology in real life applications. This motivates the attempts to find alternative techniques to automatically detect and correct ASR errors, which can be very effective and especially when the user does not have access to tune the features, the models or the decoder of the ASR system or when the transcription serves as input to downstream systems like machine translation, information retrieval, and question answering. In this paper, we present an ASR errors detection system targeted towards substitution and insertion errors. The proposed system is based on supervised learning techniques and uses input features deducted only from the ASR output words and hence should be usable with any ASR system. Applying this system on TV program transcription data leads to identify 40.30% of the recognition errors generated by the ASR system.
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: | automatic speech recognition error detection; supervised learning; ASR error detection system; ASR error correction; substitution errors; insertion errors; ASR output words; TV program transcription data |
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: | 21 Sep 2017 14:46 |
Last Modified: | 21 Mar 2018 12:44 |
Published Version: | https://doi.org/10.1109/AICCSA.2016.7945669 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/AICCSA.2016.7945669 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:121246 |