Cerebellum-like neural network for short-range timing function of a robotic speaking system

Abstract

The timing control is necessary for determining its duration, stress, and rhythm in human speech; however, little attention has been paid to these issues when building a speech synthesis system. We have developed a talking robot, which generates human-like vocal sounds. The cerebellum is an important part of human brain organ that has a significant role in the coordination, precision, and timing of motor responses. In this study, we develop a simplified cerebellumlike spiking neural network model to control the timing function for the talking robot. The model was designed using the System Generator software in Matlab, and the timing duration of trained speech was estimated using hardware cosimulated with a field programmable gate array board (FPGA). The timing information obtained from the co-simulation, together with the output motor vector, is sent to the talking robot controller to generate a sound with a short duration. The result indicates that this model can be used for short-range timing learning of the talking robot.