Development of an augmentative and alternative communication app for the Maltese language

Abstract

Augmentative and Alternative Communication (AAC) embodies all methods of communication serving as an alternative to speech. Maltese children having complex communication needs, use various AAC devices on a daily basis. Their conversation skills are mainly limited by two key factors. Firstly, AAC users communicate up to 20 times slower than people who use speech as their primary method of communication. Secondly, an AAC app for the Maltese language is currently unavailable. The aim of this work was to overcome these two limitations through the development of an AAC app targeted for the Maltese language, which provides an intelligent word suggestion mechanism to improve AAC communication rates. The app is based on a trigram language model which is able to predict the subsequent word required by the user, by considering the two previously selected words. The model was trained by means of a corpus which was specifically created for this project and uses the Interpolated Kneser-Ney smoothing technique in order to correctly resolve contexts which were not observed during training. The app enables users to retrain and update the language model, such that it may provide additional personalised word suggestions. The app was evaluated by a number of clinicians and educators who regularly work with AAC users. They remarked that it will be potentially helpful in aiding Maltese children during intervention sessions, due to its effective features. The underlying language model features an average perplexity of 90:47 when tested with non-similar training and test data and an average perplexity of 3:61 when evaluated for highly similar training and test data. The low perplexity values suggest that the language model employed in this app is remarkably accurate, and effectively performs as other trigram language models reported in literature.