CABAL a Bliss Predictive Composition Assistant for AAC Communication Software

Nicola Gatti, Matteo Matteucci

Abstract: In order to support the residual communication capabilities of verbal impaired peoples softwares allowing Augmentative and Alternative Communication (AAC) have been developed. AAC communication software aids provide verbal disables with an electronic table of AAC languages (i.e. Bliss, PCS, PIC, etc.) symbols in order to compose messages, exchange them via email, or vocally synthetize them, and so on. A current open issue, in thins kind of software, regards human-computer interaction in verbal impaired people suffering motor disorders. They can adopt only ad-hoc input device, such as buttons or switches, which require an intelligent automatic scansion of the AAC symbols table in order to compose messages. In such perspective we have developed Caba2l an innovative composition assistant exploiting an user linguistic behavior model adopting a semantic/probabilistic approach for predictive Bliss symbols scansion. Caba2l is based on an original discrete implementation of auto-regressive hidden Markov model called DAR-HMM and it is able to predict a list of symbols as the most probable ones according to both the previous selected symbol and the semantic categories associated to the symbols. We have implemented the composition assistant as a component of Bliss2003 an AAC communication software centered on Bliss language and experimentally validated it with both synthetic and real data.