Abstract
Machine recognition of spoken numbers has attracted a lot of attention because several numerical information such as account number, credit card number, telephone number can be fed to the machine conveniently in the voice mode. A number recognition system based on Artificial Neural Network is described here. The base system employs mel frequency cepstral coefficients. In this paper a simple and effective time alignment is used for spoken digit recognition systems. While high power computers are available today, time alignment algorithms, such as dynamic time warping algorithm need relatively high CPU time, which should be reserved for other complicated tasks. During recognition process, digitized speech was cleaned of noise, then the signal was pre-emphasized and it was windowed and blocked by Hamming window, the time alignment algorithm is used to compensate for the differences in the utterance length. Frames features are extracted using MFCC coefficients to reduce the amount of the information in the input signal. Finally, the neural network classifies the unknown digit.
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