Modeling language discrimination in infants using i-vector representations


Experimental research suggests that at birth infants can discriminate two languages if they belong to different rhythmic classes, and by 4 months of age they can discriminate two languages within the same class provided they have been previously exposed to at least one of them. In this paper, we present a novel application of speech technology tools to model language discrimination, which may help to understand how infants achieve high performance on this task. By combining a Gaussian Mixture Model of the acoustic space and low-dimensional representations of novel utterances with a model of a habituation paradigm, we show that brief exposure to French does not allow to discriminate between two previously unheard languages with similar phonological properties, but facilitates discrimination of two phonologically distant languages. The implications of these findings are discussed.

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