Efficient Bootstrapping of Vocalization Skills Using Active Goal Babbling

We use goal babbling, a recent approach to bootstrapping inverse models, for vowel acquisition with an articulatory speech synthesizer. In contrast to motor babbling, goal babbling organizes exploration in a low-dimensional goal space. While such a goal space is naturally given in many motor learning tasks, the difficulty in modeling speech production lies within the complexity of acoustic features. Formants can serve as low-dimensional features, but richer acoustic features are too high-dimensional to allow for an efficient goal-directed exploration. We propose to generate a low-dimensional goal space from high-dimensional features by applying dimension reduction. In this way the goal space adapts to a set of speech sounds, which models the influence from ambient speech on the speech acquisition process. Instead of pre-defining targets in this goal space, we estimate a target distribution with a Gaussian Mixture Model. We demonstrate that goal babbling can be successfully applied in this goal space in order to learn a parametric model of vowel production. By augmenting goal-directed exploration with an active selection of targets, we achieve a significant speed-up in learning.