Adaptive speech recognition with evolving connectionist systems

Abstract

The paper presents a novel approach towards building adaptive speech recognition systems based on the evolving connectionist systems paradigm (ECoS). The simple evolving connectionist systems are the minimalist implementation of the ECoS. They can accommodate new input data and new classes through local element tuning. New connections and neurons are created during the adaptive learning process of the system. Experiments are conducted to illustrate this concept. It is demonstrated that a system can adapt to new speakers data and add new output classes on-line, e.g. new words, added at any time of its operation without having to rebuild the network from “scratch”. The system is robust to forgetting when new words are added.