Multimodal multispeaker probabilistic tracking in meetings

Tracking speakers in multiparty conversations constitutes a fundamental task for automatic meeting analysis. In this paper, we present a probabilistic approach to jointly track the location and speaking activity of multiple speakers in a multisensor meeting room, equipped with a small microphone array and multiple uncalibrated cameras. Our framework is based on a mixed-state dynamic graphical model defined on a multiperson state-space, which includes the explicit definition of a proximity-based interaction model. The model integrates audio-visual (AV) data through a novel observation model. Audio observations are derived from a source localization algorithm. Visual observations are based on models of the shape and spatial structure of human heads. Approximate inference in our model, needed given its complexity, is performed with a Markov Chain Monte Carlo particle filter (MCMC-PF), which results in high sampling efficiency. We present results -based on an objective evaluation proce...

• Type: Conference
• Year: 2005
• Where: ICMI
• Authors: Daniel Gatica-Perez, Guillaume Lathoud, Jean-Marc Odobez, Iain McCowan
• Keywords: ICMI 2005, Markov Chain Monte Carlo, Multiple Uncalibrated Cameras, Speaking Activity