Alharbi, S., Hasan, M., Simons, A.J.H. orcid.org/0000-0002-5925-7148 et al. (2 more authors) (2018) A lightly supervised approach to detect stuttering in children's speech. In: Proceedings of Interspeech 2018. Interspeech 2018, 02-06 Sep 2018, Hyderabad, India. ISCA , pp. 3433-3437.
Abstract
© 2018 International Speech Communication Association. All rights reserved. In speech pathology, new assistive technologies using ASR and machine learning approaches are being developed for detecting speech disorder events. Classically-trained ASR model tends to remove disfluencies from spoken utterances, due to its focus on producing clean and readable text output. However, diagnostic systems need to be able to track speech disfluencies, such as stuttering events, in order to determine the severity level of stuttering. To achieve this, ASR systems must be adapted to recognise full verbatim utterances, including pseudo-words and non-meaningful part-words. This work proposes a training regime to address this problem, and preserve a full verbatim output of stuttering speech. We use a lightly-supervised approach using task-oriented lattices to recognise the stuttering speech of children performing a standard reading task. This approach improved the WER by 27.8% relative to a baseline that uses word-lattices generated from the original prompt. The improved results preserved 63% of stuttering events (including sound, word, part-word and phrase repetition, and revision). This work also proposes a separate correction layer on top of the ASR that detects prolongation events (which are poorly recog-nised by the ASR). This increases the percentage of preserved stuttering events to 70%.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2018 ISCA. Reproduced in accordance with the publisher's self-archiving policy. |
Dates: |
|
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: | 01 Nov 2018 09:35 |
Last Modified: | 01 Nov 2018 09:35 |
Published Version: | https://doi.org/10.21437/Interspeech.2018-2155 |
Status: | Published |
Publisher: | ISCA |
Refereed: | Yes |
Identification Number: | 10.21437/Interspeech.2018-2155 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:137999 |