During the early stages of language acquisition, young infants face the task of learning a basic vocabulary without the aid of prior linguistic knowledge. It is believed the long term episodic memory plays an important role in this process. Experiments have shown that infants retain large amounts of very detailed episodic information about the speech they perceive (e.g. ). This weakly justifies the fact that some algorithms attempting to model the process of vocabulary acquisition computationally process large amounts of speech data in batch. Non-negative Matrix Factorization (NMF), a technique that is particularly successful in data mining but can also be applied to vocabulary acquisition (e.g. ), is such an algorithm. In this paper, we will integrate an adaptive variant of NMF into a computational framework for vocabulary acquisition, foregoing the need for long term storage of speech inputs, and experimentally show its accuracy matches that of the original batch algorithm.
Bibliographic reference. Driesen, Joris / Bosch, L. ten / Van hamme, Hugo (2009): "Adaptive non-negative matrix factorization in a computational model of language acquisition", In INTERSPEECH-2009, 1731-1734.