A Bidirectional Graphical Model for Babble-feedback Learning in Speech

A theory of motor-feedback learning based on a bidirectional-edge graph is summarized. The graph is inspired as a model of the cortico-thalamic circuit. Output from the network drives the motors of an articulatory speech synthesizer. The network is first exposed to an ongoing stream of speech sounds where neural fields tune themselves to respond to repetitive co-occurring patterns in the input (phonetic features). The network is then made to actuate motors with babble feedback learning in an effort to train the network to produce the speech sounds and strings of sounds that the network was originally exposed to. The model furthermore makes use of a bank of resonators, intended to depict the functional role of the cerebellum. This cerebellar model helps in coordinating the longer-term timing of network dynamics and helps to smoothly string together patterns of speech sounds through time.