A study on the recognition of the Korean monothongs using artificial neural net models

The implementation and comparison of various artificial neural network models for recognition of Korean monothongs are reported. The goal is to develop an intelligent speech-based man-machine computer interface. The neural networks used were the multilayer perceptron, the time-delay neural net, the self-organizing feature map, and the interactive and competitive model. These four models were compared with respect to recognition rate and learning speed under various conditions. Experiments taking context effects, the most important problem in recognizing phonemes from continuous speech, into consideration were also performed. The models showed 90%-96% recognition rate for a male speaker. A strategy for Korean speech recognition using artificial neural networks has been developed on the basis of these results