This paper shows how an articulatory model, able to produce acoustic signals from articulatory motion, can learn to speak, i.e. coordinate its movements in such a way that it utters meaningful sequences of sounds belonging to a given language. This complex learning procedure is accomplished in four major steps: (a) a babbling phase, where the device builds up a model of the forward transforms, i.e. the articulatory-to-audio-visual mapping; (b) an imitation stage, where it tries to reproduce a limited set of sound sequences by audio-visual-to-articulatory inversion; (c) a "shaping" stage, where phonemes are associated with the most efficient available sensori-motor representation; and finally, (d) a "rhythmic" phase, where it learns the appropriate coordination of the activations of these sensori-motor targets.

This thesis describes HABLAR, a computational model of the sensorimotor foundations of early childhood phonological development. HABLAR (an acronym for "Hierarchical Articulatory Based Language Acquisition by Reinforcement learning" and Spanish for "to speak") is intended to replicate the major milestones of emerging speech and demonstrate key characteristics of normal development, including the phonetic characteristics of babble, systematic and context-sensitive patterns of sound substitutions and deletions, overgeneralization errors, and the emergence of adult phonemic organization. It should also mimic abnormal phonological development under certain conditions of damage or degradation. HABLAR simulates a complete sensorimotor system consisting of an auditory system that detects and categorizes speech sounds using only acoustic cues drawn from its linguistic environment, an articulatory system that generates synthetic speech based on a realistic computer model of the vocal tract, an...