Speaker tracking using multi-modal fusion framework

Abstract

This paper introduces a framework by which multi-modal sensory data can be efficiently and meaningfully combined in the application of speaker tracking. This framework fuses together four different observation types taken from multi-modal sensors. The advantages of this fusion are that weak sensory data from either modality can be reinforced, and the presence of noise can be reduced. We propose a method of combining these modalities by employing a particle filter. This method offers satisfied real-time performance. © 2012 Springer-Verlag.