Connectionist architectural learning for high performance character and speech recognition

The authors applied an automatic structure optimization (ASO) algorithm to the optimization of multistate time-delay neural networks (MSTDNNs), an extension of the TDNN. These networks allow the recognition of sequences of ordered events that have to be observed jointly. For example, in many speech recognition systems the recognition of words is decomposed into the recognition of sequences of phonemes or phonemelike units. In handwritten character recognition the recognition of characters can be decomposed into the joined recognition of characteristic strokes, etc. The combination of the proposed ASO algorithm with the MSTDNN was applied successfully to speech recognition and handwritten character recognition tasks with varying amounts of training data.<>