Automatically learning measures of child language development

Authors:
Sam Sahakian;Benjamin Snyder
Affiliations:
University of Wisconsin - Madison;University of Wisconsin - Madison
Venue:
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Year:
2012

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Abstract

We propose a new approach for the creation of child language development metrics. A set of linguistic features is computed on child speech samples and used as input in two age prediction experiments. In the first experiment, we learn a child-specific metric and predicts the ages at which speech samples were produced. We then learn a more general developmental index by applying our method across children, predicting relative temporal orderings of speech samples. In both cases we compare our results with established measures of language development, showing improvements in age prediction performance.