DAMA: A Dynamic Classification of Multimodal Ambiguities

Abstract

Ambiguities represent uncertainty but also a fundamental item of discussion for who is interested in the interpretation of languages and it is actually functional for communicative purposes both in human–human communication and in human–machine interaction. This paper faces the need to address ambiguity issues in human–machine interaction. It deals with the identification of the meaningful features of multimodal ambiguities and proposes a dynamic classification method that characterizes them by learning, and progressively adapting with the evolution of the interaction language, by refining the existing classes, or by identifying new ones. A new class of ambiguities can be added by identifying and validating the meaningful features that characterize and distinguish it compared to the existing ones. The experimental results demonstrate improvement in the classification rate over considering new ambiguity classes.

Keywords

• Hidden Markov models
• Human–machine interaction
• Multimodal interaction
• Natural language processing