Pattern recognition and text dependent voice recognition system for a biped robot

Pattern Recognition and Audio Processing are important aspects in the control and behavior of mobile robots; their action depends on the recognition of visual and audio stimuli in order to reflect intelligent behavior. This work presents an economic intelligent recognition system developed in an integrated manner for a biped robot. It consists of two main parts: one is the isolated word and pitch recognition, using Backpropagation Neural Networks (BNN) in which input signals are out coming from a Linear Predictive Coding (LPC) processing method that characterizes each voice signal. The second consists of an intelligent, artificially vision based, tracking system that will be activated with a specified voice command which tracks the moving body in front of it, keeping it in the field of view. Consecutive images must be acquired and processed in the time domain as well as in the frequency domain. The objective is to design a person tracking system for a mobile robot with text dependent pitch recognition and a visual pattern recognition mechanism. The BNN will awake the robot from the idle position, while the visual stimulation will be used to track the person when given the command in real time.