Robot speech learning via entropy guided LVQ and memory association

Abstract

The goal of this project is to teach a computer-robot system to understand human speech through natural human-computer interaction. To achieve this goal, we develop an interactive and incremental learning algorithm based on entropy-guided learning vector quantisation (LVQ) and memory association. Supported by this algorithm, the robot has the potential to learn unlimited sounds progressively. Experimental results of a multilingual short-speech learning task are given after the presentation of the learning system. Further investigation of this learning system will include human-computer interactions that involve more modalities, and applications that use the proposed idea to train home appliances.