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Estimating the positions of multiple speakers can be helpful for tasks likeautomatic speech recognition or speaker diarization. Both applications benefitfrom a known speaker position when, for instance, applying beamforming orassigning unique speaker identities. Recently, several approaches utilizingacoustic signals augmented with visual data have been proposed for this task.However, both the acoustic and the visual modality may be corrupted in specificspatial regions, for instance due to poor lighting conditions or to thepresence of background noise. This paper proposes a novel audiovisual datafusion framework for speaker localization by assigning individual dynamicstream weights to specific regions in the localization space. This fusion isachieved via a neural network, which combines the predictions of individualaudio and video trackers based on their time- and location-dependentreliability. A performance evaluation using audiovisual recordings yieldspromising results, with the proposed fusion approach outperforming all baselinemodels.

Data Fusion for Audiovisual Speaker Localization: Extending Dynamic Stream Weights to the Spatial Domain