DUNEDIN NEW ZEALAND Spatial-Temporal Adaptation in Evolving Fuzzy Neural Networks for On-line Adaptive Phoneme Recognition

The paper is a study on a new class of spatial-temporal evolving fuzzy neural network systems (EFuNNs) for on-line adaptive learning, and their applications for adaptive phoneme recognition. The systems evolve through incremental, hybrid (supervised / unsupervised) learning. They accommodate new input data, including new features, new classes, etc. through local element tuning. Both feature-based similarities and temporal dependencies, that are present in the input data, are learned and stored in the connections, and adjusted over time. This is an important requirement for the task of adaptive, speaker independent spoken language recognition, where new pronunciations and new accents need to be learned in an on-line, adaptive mode. Experiments with EFuNNs, and