Learning Hidden Curved Exponential Family Models to Infer Face-to-Face Interaction Networks from Situated Speech Data

Danny Wyatt, Tanzeem Choudhury, Jeff Bilmes

In this paper, we present a novel probabilistic framework for recovering global, latent social network structure from local, noisy observations. We extend curved exponential random graph models to include two types of variables: hidden variables that capture the structure of the network and observational variables that capture the behavior between actors in the network. We develop a novel combination of informative and intuitive conversational (local) and structural (global) features to specify our model. The model learns, in an unsupervised manner, the relationship between observable behavior and hidden social structure while simultaneously learning properties of the latent structure itself. We present empirical results on both synthetic data and a real world dataset of face-to-face conversations collected from 24 individuals using wearable sensors over the course of 6 months.

Subjects: 12. Machine Learning and Discovery; 3.4 Probabilistic Reasoning

Submitted: Apr 15, 2008

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