Learning from a tutor: Embodied speech acquisition and imitation learning

This work presents a new developmentally inspired data-driven framework to bootstrap speech perception and imitation abilities in interaction with a tutor. The proposed system architecture extends our work presented in, that implements a cascade of interconnected layers to acquire the structure of speech in terms of phones, syllables and words. Here, we show how to couple such a perceptual model with a speech imitation system that is based on an acoustic synthesizer bound to produce speech sounds with a child´s voice. We present an interaction scheme to learn the correspondence model between the tutor´s and the system´s voice. It acquires such a model by linking its own phonemic primitives to the imitating feedback from its tutor. The model can then be used to map a tutor´s utterance into a continuous activity in the system´s motor space. Furthermore, we propose how such motor activities can be used to imitate words. Finally, we embed this architecture into an embodied autonomous learning and interaction system to provide a grounding for the speech models to be acquired and a perceptual input to trigger speech production.