Ecological language acquisition via incremental model-based clustering

We analyse the behaviour of Incremental Model-Based Clustering on child-directed speech data, and suggest a possible use of this method to describe the acquisition of phonetic classes by an infant. The effects of two factors are analysed, namely the number of coefficients describing the speech signal, and the frame length of the incremental clustering procedure. The results show that, although the number of predicted clusters vary in different conditions, the classifications obtained are essentially consistent. Different classifications were compared using the variation of information measure.