Probabilistic models for concurrent chatting activity recognition

Jane Yung Jen Hsu, Chia Chun Lian, Wan Rong Jih

研究成果: 期刊稿件文章同行評審

9 引文 斯高帕斯(Scopus)

摘要

Recognition of chatting activities in social interactions is useful for constructing human social networks. However, the existence of multiple people involved in multiple dialogues presents special challenges. To model the conversational dynamics of concurrent chatting behaviors, this article advocates Factorial Conditional Random Fields (FCRFs) as a model to accommodate co-temporal relationships among multiple activity states. In addition, to avoid the use of inefficient Loopy Belief Propagation (LBP) algorithm, we propose using Iterative Classification Algorithm (ICA) as the inference method for FCRFs. We designed experiments to compare our FCRFs model with two dynamic probabilistic models, Parallel Condition Random Fields (PCRFs) and Hidden Markov Models (HMMs), in learning and decoding based on auditory data. The experimental results show that FCRFs outperform PCRFs and HMMs-like models. We also discover that FCRFs using the ICA inference approach not only improves the recognition accuracy but also takes significantly less time than the LBP inference method.

原文英語
文章編號4
期刊ACM Transactions on Intelligent Systems and Technology
2
發行號1
DOIs
出版狀態已出版 - 01 2011
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