Abstract
A speaker-dependent, isolated-word speech recognition system is presented which is based on the use of the fast Fourier transform for extracting features from the speech input. The algorithm then normalizes those features and compares them against previously stored word templates using dynamic time warping in order to identify the uttered word. The system has been successfully implemented and provided good results when tested using a small dictionary.
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