Context induced merging of synonymous word models in computational modeling of early language acquisition

It has been shown that both infants and machines are able to discover recurring word-like patterns from continuous speech in the absence of supervision. However, these early models for words do not always generalize well across different acoustic variants of the same words. Instead, several parallel models for words or multiple fragments of a word are initially learned. In this work, we study a two-stage computational framework for refining the initially acquired representations of acoustic word patterns. In the first stage, the automatically discovered word patterns are studied in the context of visual word referents, enabling grounding of the word forms to the systematically co-occurring objects and actions in the environment. In the second stage, synonymy of the words is measured in terms of the similarity of their environmental contexts. The word models that share similar external context are merged together, producing a lexicon with a smaller number of parallel models for each word and with a greater generalization capability from each model towards new realizations of the word. The experimental results show that the context-based equivalence and merging of parallel models leads to a more compact and higher quality lexicon than a learning process based purely on acoustic similarities.