Hybrid intelligent adaptive systems: A framework and a case study on speech recognition

This paper explores a multi-modular architecture of an intelligent information system and proposes a method for adaptation in it. The method is based on evaluating which of the modules need to be adapted based on the performance of the whole system on new data. These modules are then trained selectively on the new data until they improve their performance and the performance of the whole system. The modules are fuzzy neural networks, especially designed to facilitate adaptive training and knowledge discovery, and spatial temporal maps. A particular case study of spoken language recognition is presented along with some preliminary experimental results of an adaptive speech recognition system.