Providing Intelligent Language Feedback for Augmentative Communication Users

People with severe speech and motor impairments (SSMI) can often use augmentative communication devices to help them communicate. While these devices can provide speech synthesis or text output, the rate of communication is typically very slow. Consequently, augmentative communication users often develop telegraphic patterns of language usage. A natural language processing technique termed compansion (compression-expansion) has been developed that expands unin ected content words (i.e., compressed or telegraphic utterances) into syntactically and semantically well-formed sentences. While originally designed as a rate enhancement technique, compansion may also be viewed as a potential tool to support English literacy for augmentative communication users. Accurate grammatical feedback from ill-formed inputs might be very bene cial in the learning process. However, the problems of dealing with inherently ambiguous errors and multiple corrections are not trivial. This paper proposes the addition of an adaptive user language model as a way to address some of these di culties. It also discusses a possible implementation strategy using grammatical mal-rules for a prototype application that uses the compansion technique.