Skip navigation
Please use this identifier to cite or link to this item:

acessibilidade

http://hdl.handle.net/123456789/4373
wcag
Title: Word and sentence prediction: Using the best of the two worlds to assist AAC users
Authors: García, Luís
Oliveira, Luis
Matos, David
Keywords: Augmentative and alternative communication
rate enhancement techniques
sentence prediction
vocabulary prediction
word prediction
Issue Date: 2014
Publisher: IOS Press
Citation: [IEEE style] L. Garcia, L. De Oliveira, D. De Matos, “Word and sentence prediction: Using the best of the two worlds to assist AAC users “, in Technology and Disability, 2014, 79-91.
Abstract: BACKGROUND: Persons who rely on Augmentative and Alternative Communication (AAC) systems face tremendous difficulties to maintain a conversation, in part as a consequence of a poor output rate. Word Prediction is a popular AAC technique that can save up around 50% keystrokes. However, only modest communication rate improvements have been reported in the literature. OBJECTIVE: Therefore, we studied the effect of using Sentence Prediction as a complementary, and faster technique, to Word Prediction, in a text-based AAC system. METHODS: To evaluate this strategy we conducted user tests with a Word and Sentence Prediction prototype we have been developing for a client from a local rehabilitation center. Communication rate was measured with the system having full and partial knowledge of sentences to be composed. RESULTS: With able bodied non-AAC users, mean rates of 18.8 WPM and 21.0 WPM were obtained, respectively, combining Sentence Prediction with Word Prediction, and using Sentence Prediction only. The Sentence Prediction with Word Prediction was the fastest configuration for the AAC user participant, with 7.2 WPM. These results were obtained with the system having knowledge about all the sentences the subjects had to produce (100% sentence knowledge). In a subsequent test, sentence knowledge conditions were degraded to measure performance under non-ideal conditions. The conditions with less sentence knowledge (25% and 0%) had results close to the Word Prediction only condition, around 8 WPM for the able bodied users and 1.3 WPM for the AAC user, which is an indicator that under low sentence reuse conditions Sentence Prediction does not compromise user performance. CONCLUSIONS: Since Sentence Prediction can potentially improve communication rate, we think this technique should be considered as a valuable complement to Word Prediction on text-prediction AAC solutions.
Peer reviewed: yes
URI: https://repositorio.ipbeja.pt/handle/123456789/4373
ISSN: 10554181
Publisher version: http://dx.doi.org10.3233/TAD-140406
Appears in Collections:D-ENG - Artigos em revistas com peer review

Files in This Item:
There are no files associated with this item.


FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpace
Formato BibTex MendeleyEndnote Currículo DeGóis 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.