Evaluating parameters for mapping adult vowels to imitative babbling

We design a neural network model of first language acquisi-tion to explore the relationship between child and adult speech sounds. The model learns simple vowel categories using a produce-and-perceive babbling algorithm in addition to listen-ing to ambient speech. The model is similar to that of Wester-mann & Miranda (2004), but adds a dynamic aspect in that it adapts in both the articulatory and acoustic domains to changes in the child’s speech patterns. The training data is designed to replicate infant speech sounds and articulatory configurations. By exploring a range of articulatory and acoustic dimensions, we see how the child might learn to draw correspondences be-tween his or her own speech and that of a caretaker, whose productions are quite different from the child’s. We also de-sign an imitation evaluation paradigm that gives insight into the strengths and weaknesses of the model. Index Terms: language acquisition, neural networks, self-organizing maps, language developmen