Word and Sentence Prediction: Using the best of the two worlds to assist AAC users

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.