Table 1. Speech Recognition Performances (alphabet Recognition)

Highly structured artificial neural networks have been shown to be superior to fully connected networks for realworld applications like speech recognition and handwritten character recognition. These structured networks can be optimized in many ways, and have to be optimized for optimal performance. This makes the manual optimization very timeconsuming. A highly structured approach is the Multi State Time Delay Neural Network (MSTDNN) which uses shifted input windows and allows the recognition of sequences of ordered events that have to be observed jointly. In this paper we propose an Automatic Structure Optimization (ASO) algorithm and apply it to MSTDNN type networks. The ASO algorithm optimizes all relevant parameters of MSTDNNs automatically and was successfully tested with three different tasks and varying amounts of training data.