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The impressive ability of children to acquire language is a widely studiedphenomenon, and the factors influencing the pace and patterns of word learningremains a subject of active research. Although many models predicting the ageof acquisition of words have been proposed, little emphasis has been directedto the raw input children achieve. In this work we present a comparativelylarge-scale multi-modal corpus of prosody-text aligned child directed speech.Our corpus contains automatically extracted word-level prosodic features, andwe investigate the utility of this information as predictors of age ofacquisition. We show that prosody features boost predictive power in aregularized regression, and demonstrate their utility in the context of amulti-modal factorized language models trained and tested on child-directedspeech.

Prosodic Features from Large Corpora of Child-Directed Speech as Predictors of the Age of Acquisition of Words