Towards shallow grammar induction for an adaptive assistive vocal interface : a concept tagging approach

This paper describes research within the ALADIN project, which aims to develop an adaptive, assistive vocal interface for people with a physical impairment. One of the components in this interface is a self-learning grammar module, which maps a user’s utterance to its intended meaning. This paper describes a case study of the learnability of this task on the basis of a corpus of commands for the card game patience. The collection, transcription and annotation of this corpus is outlined in this paper, followed by results of preliminary experiments using a shallow concept-tagging approach. Encouraging results are observed during learning curve experiments, that gauge the minimal amount of training data needed to trigger accurate concept tagging of previously unseen utterances.