A Corpus for a Gesture-Controlled Mobile Spoken Dialogue System

Speech and hand gestures offer the most natural modalities for everyday human-to-human interaction. The availability of diverse spoken dialogue applications and the proliferation of accelerometers on consumer electronics allow the introduction of new interaction paradigms based on speech and gestures. Little attention has been paid however to the manipulation of spoken dialogue systems through gestures. Situation-induced disabilities or real disabilities are determinant factors that motivate this type of interaction. In this paper we propose six concise and intuitively meaningful gestures that can be used to trigger the commands in any SDS. Using different machine learning techniques we achieve a classification error for the gesture patterns of less than 5%, and we also compare our own set of gestures to ones proposed by users. Finally, we examine the social acceptability of the specific interaction scheme and encounter high levels of acceptance for public use.