Adaptive non-negative matrix factorization in a computational model of language acquisition

During the early stages o f 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 o f very detailed episodic information about the speech they perceive (e.g. [1]). This weakly justifies the fact that some algorithms attempting to model the process o f vocabulary acquisition computationally process large amounts o f 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. [2]), is such an algorithm. In this paper, we will integrate an adaptive variant o f 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 o f the original batch algorithm.