Automatic Meeting Segmentation Using Dynamic Bayesian Networks

Multistream Dynamic Bayesian Network for Meeting Segmentation

This paper investigates the automatic analysis and segmentation of meetings. A meeting is analysed in terms of individual behaviours and group interactions, in order to decompose each meeting in a sequence of relevant phases, named meeting actions. Three feature families are extracted from multimodal recordings: prosody from individual lapel microphone signals, speaker activity from microphone ...

Automatic Speech Recognition using Dynamic Bayesian Networks

New ideas to improve automatic speech recognition have been proposed that make use of context user information such as gender, age and dialect. To incorporate this information into a speech recognition system a new framework is being developed at the mmi department of the ewi faculty at the Delft University of Technology. This toolkit is called Gaia and makes use of Dynamic Bayesian networks. I...

Motion Segmentation Using Inference in Dynamic Bayesian Networks

Existing formulations for optical flow estimation and image segmentation have used Bayesian Networks and Markov Random Field (MRF) priors to impose smoothness of segmentation. These approaches typically focus on estimation in a single time slice based on two consecutive images. We develop a motion segmentation framework for a continuous stream of images using inference in a corresponding Dynami...

Automatic Speech Recognition using Pitch Information in Dynamic Bayesian Networks

Automatic recognition of multiparty human interactions using dynamic Bayesian networks

Relating statistical machine learning approaches to the automatic analysis of multiparty communicative events, such as meetings, is an ambitious research area. We have investigated automatic meeting segmentation both in terms of “Meeting Actions” and “Dialogue Acts”. Dialogue acts model the discourse structure at a fine grained level highlighting individual speaker intentions. Group meeting act...