Building a talking baby robot

Speech is a perceptuo-motor system. A natural computational modeling framework is provided by cognitive robotics, or more precisely speech robotics, which is also based on embodiment, multimodality, development, and interaction. This paper describes the bases of a virtual baby robot which consists in an articulatory model that integrates the non-uniform growth of the vocal tract, a set of sensors, and a learning model. The articulatory model delivers sagittal contour, lip shape and acoustic formants from seven input parameters that characterize the configurations of the jaw, the tongue, the lips and the larynx. To simulate the growth of the vocal tract from birth to adulthood, a process modifies the longitudinal dimension of the vocal tract shape as a function of age. The auditory system of the robot comprises a “phasic” system for event detection over time, and a “tonic” system to track formants. The model of visual perception specifies the basic lips characteristics: height, width, area and protrusion. The orosensorial channel, which provides the tactile sensation on the lips, the tongue and the palate, is elaborated as a model for the prediction of tongue-palatal contacts from articulatory commands. Learning involves Bayesian programming, in which there are two phases: (i) specification of the variables, decomposition of the joint distribution and identification of the free parameters through exploration of a learning set, and (ii) utilization which relies on questions about the joint distribution.

Two studies were performed with this system. Each of them focused on one of the two basic mechanisms, which ought to be at work in the initial periods of speech acquisition, namely vocal exploration and vocal imitation. The first study attempted to assess infants’ motor skills before and at the beginning of canonical babbling. It used the model to infer the acoustic regions, the articulatory degrees of freedom and the vocal tract shapes that are the likeliest explored by actual infants according to their vocalizations. Subsequently, the aim was to simulate data reported in the literature on early vocal imitation, in order to test whether and how the robot was able to reproduce them and to gain some insights into the actual cognitive representations that might be involved in this behavior.

Speech modeling in a robotics framework should contribute to a computational approach of sensori-motor interactions in speech communication, which seems crucial for future progress in the study of speech and language ontogeny and phylogeny.