Goal babbling of acoustic-articulatory models with adaptive exploration noise

We use goal babbling to bootstrap a parametric model of speech production for a complex 3D vocal tract model. The system learns to control the articulators for producing five different vowel sounds. Ambient speech influences learning on two levels: it organizes the learning process because it is used to generate a space of goals in which exploration takes place. A distribution learned from ambient speech provides the system with targets during exploration. Previous research with this vocal tract model showed that visual information have to be included for acquiring the vowel [u] via reward-based optimization. We model the learning process instead with goal-directed exploration where all targets are learned in parallel. As some vowels require more exploratory noise in the articulators than others, we propose a mechanism to adapt the noise amplitude depending on the system's competence in different regions of the goal space. We demonstrate that this self-aware learning leads to more stable results. The implemented system succeeds in acquiring vocalization skills for rounded as well as unrounded vowels using only a single modality.