Learning Word Meanings and Grammar for Describing Everyday Activities in Smart Environments

Abstract

If intelligent systems are to interact with humans in a natural manner, the ability to describe daily life activities is important. To achieve this, sensing human activities by capturing multimodal information is necessary. In this study, we consider a smart environment for sensing activities with respect to realistic scenarios. We next propose a sentence generation system from observed multimodal information in a bottom up manner using multilayered multimodal latent Dirichlet allocation and Bayesian hidden Markov models. We evaluate the grammar learning and sentence generation as a complete process within a realistic setting. The experimental result reveals the effectiveness of the proposed method.