Cross-situational noun and adjective learning in an interactive scenario

Yuxin Chen 1, 2 David Filliat 1, 2
1 Flowers - Flowing Epigenetic Robots and Systems
Inria Bordeaux - Sud-Ouest, U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
Abstract : Learning word meanings during natural interaction with a human faces noise and ambiguity that can be solved by analysing regularities across different situations. We propose a model of this cross-situational learning capacity and apply it to learning nouns and adjectives from noisy and ambiguous speeches and continuous visual input. This model uses two different strategy: a statistical filtering to remove noise in the speech part and the Non Negative Matrix Factorization algorithm to discover word-meaning in the visual domain. We present experiments on learning object names and color names showing the performance of the model in real interactions with humans, dealing in particular with strong noise in the speech recognition.
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Submitted on : Tuesday, August 18, 2015 - 4:00:32 PM
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Yuxin Chen, David Filliat. Cross-situational noun and adjective learning in an interactive scenario. Joint IEEE International Conference Developmental Learning and Epigenetic Robotics (ICDL-EPIROB), Aug 2015, Providence, United States. ⟨hal-01170674⟩



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