Yusufali, H. orcid.org/0000-0001-6848-5495, Goetze, S. and Moore, R. (2023) Bridging the communication rate gap: enhancing text input for augmentative and alternative communication (AAC). In: Gao, Q., Zhou, J., Duffy, V.G., Antona, M. and Stephanidis, C., (eds.) HCI International 2023 – Late Breaking Papers. 25th International Conference on Human-Computer Interaction (HCII 2023), 23-28 Jul 2023, Copenhagen, Denmark. Lecture Notes in Computer Science, 14055 . Springer Cham ISBN 9783031356810
Abstract
Over 70 million people worldwide face communication difficulties, with many using augmentative and alternative communication (AAC) technology. While AAC systems help improve interaction, the communication rate gap between individuals with and without speaking difficulties remains significant, and this has led to a low sustained use of AAC systems. The study reported here combines human computer interaction (HCI) and language modelling techniques to improve the ease of use, user satisfaction, and communication rates of AAC technology in open-domain interactions. A text input interface utilising word prediction based on BERT and RoBERTa language models has been investigated with a view to improving communication rates. Three interface layouts were implemented, and it was found that a radial configuration was the most efficient. RoBERTa models fine-tuned on conversational AAC corpora led to the highest communication rates of 25.75 words per minute (WPM), with alphabetical ordering preferred over probabilistic ordering. It was also found that training on conversational corpora such as TV and Reddit outperformed training based on generic corpora such as COCA or Wikipedia. Hence, it is concluded that the limited availability of large-scale conversational AAC corpora represent a key challenge for improving communication rates and robust AAC systems.
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: | © 2023 The Author(s). This is an author-produced version of a proceedings paper subsequently published in HCI International 2023 – Late Breaking Papers. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Text Input Prediction; Language Modelling; Augmentative and Alternative Communication (AAC); Speech Synthesis |
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 Engineering and Physical Sciences Research Council 2268211 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 Jul 2023 15:13 |
Last Modified: | 02 Dec 2024 01:13 |
Status: | Published |
Publisher: | Springer Cham |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-031-48041-6_29 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:201253 |