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Continuous digits recognition using spatio-temporal neural network

In this paper, a new approach for continuous digits recognition using the Spatio-Temporal Neural Network (STNN) is reported. The continuous seven digits are gargeted to recognize, and our initial recognition rate was 28%. In this paper, to increase the recognition rate, two methods are proposed. In the first method, to compensated the STNN's own defect as well as to emphasize the Korean digits' phonic characteristics, the starting point ofeach digit is detected using the energy and zero-crossing rate, but the ending point is detectedonly using the energy value. In this case, the seven digits recognition reate increased to 61%. Furthermore, in the second method, considering the fact that a same digit could be pronounced differently in continuously spoken environment, the number of STNNs used to represent each digit is increased from one to five. Consequently, the same digit but pronounced differently could be handled well in the new system. As a result of that, the continuously spoken seven digits recognition rate increased to 89%.