Nicolao, M., Beeston, A.V. orcid.org/0000-0003-2796-1947 and Hain, T. orcid.org/0000-0003-0939-3464 (2015) Automatic assessment of English learner pronunciation using discriminative classifiers. In: Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on. 40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 19-24 Apr 2015, Brisbane, Australia. IEEE , pp. 5351-5355.
Abstract
This paper presents a novel system for automatic assessment of pronunciation quality of English learner speech, based on deep neural network (DNN) features and phoneme specific discriminative classifiers. DNNs trained on a large corpus of native and non-native learner speech are used to extract phoneme posterior probabilities. A part of the corpus includes per phone teacher annotations, which allows training of two Gaussian Mixture Models (GMM), representing correct pronunciations and typical error patterns. The likelihood ratio is then obtained for each observed phone. Several models were evaluated on a large corpus of English-learning students, with a variety of skill levels, and aged 13 upwards. The cross-correlation of the best system and average human annotator reference scores is 0.72, with miss and false alarm rate around 19%. Automatic assessment is 81.6% correct with a high degree of confidence. The new approach significantly outperforms spectral distance based baseline systems.
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 Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on. 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) |
Funding Information: | Funder Grant number ITSLANGUAGE BV none |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 21 Feb 2017 13:26 |
Last Modified: | 29 Mar 2018 07:25 |
Published Version: | https://doi.org/10.1109/ICASSP.2015.7178993 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ICASSP.2015.7178993 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:109268 |