Alharbi, S., Hasan, M., Simons, A.J.H. orcid.org/0000-0002-5925-7148 et al. (2 more authors) (2017) Detecting stuttering events in transcripts of children’s speech. In: Camelin, N., Estève, Y. and Martín-Vide, C., (eds.) SLSP 2017: Statistical Language and Speech Processing. International Conference on Statistical Language and Speech Processing 2017, 23-25 Oct 2017, Le Mans, France. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10583 (volume 10583). Springer International Publishing , pp. 217-228. ISBN 9783319684550
Abstract
Stuttering is a common problem in childhood that may persist into adulthood if not treated in early stages. Techniques from spoken language understanding may be applied to provide automated diagnosis of stuttering from children speech. The main challenges however lie in the lack of training data and the high dimensionality of this data. This study investigates the applicability of machine learning approaches for detecting stuttering events in transcripts. Two machine learning approaches were applied, namely HELM and CRF. The performance of these two approaches are compared, and the effect of data augmentation is examined in both approaches. Experimental results show that CRF outperforms HELM by 2.2% in the baseline experiments. Data augmentation helps improve systems performance, especially for rarely available events. In addition to the annotated augmented data, this study also adds annotated human transcriptions from real stuttered children’s speech to help expand the research in this field.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2017 Springer International Publishing AG. This is an author produced version of a paper subsequently published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. The final publication is available at Springer via https://doi.org/10.1007/978-3-319-68456-7_18. |
Keywords: | Stuttering event detection; Speech disorder; Human-computer interaction; CRF; HELM |
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: | 22 Nov 2017 11:07 |
Last Modified: | 22 Nov 2017 11:07 |
Published Version: | https://doi.org/10.1007/978-3-319-68456-7_18 |
Status: | Published |
Publisher: | Springer International Publishing |
Series Name: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-68456-7_18 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:124202 |