Dealing with uncertain input in word learning
Versteegh, M., Ten Bosch, L., & Boves, L.
Dealing with uncertain input in word learning. In Proceedings of the IXth IEEE International Conference on Development and Learning (ICDL). Ann Arbor, MI, 18-21 Aug. 2010
(pp. 46-51). IEEE.
In this paper we investigate a computational model of word learning, that is embedded in a cognitively and ecologically plausible framework. Multi-modal stimuli from four different speakers form a varied source of experience. The model incorporates active learning, attention to a communicative setting and clarity of the visual scene. The model's ability to learn associations between speech utterances and visual concepts is evaluated during training to investigate the influence of active learning under conditions of uncertain input. The results show the importance of shared attention in word learning and the model's robustness against noise.