Author: Andrew Kingston
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Two popular approaches to isolated word speech recognition include the template matching and the neural network approach. An implementation of each of these systems is presented in this report, and a comparison between the two is given. The recognition task is limited to digits only, and experiments involving a single speaker and a more difficult multiple speaker task were performed. In constructing the input for the recognition systems, various digital signal processing routines that transform the raw speech signal into a comparable word template format are examined. Word boundary detection is investigated, and an algorithm was developed to automatically separate each digit from the collected speech samples. A ``dynamic time warping'' procedure provides the template matching system with the ability to time align two templates, in contrast to the ``temporal flow'' neural network model, which contains a network of delay links to represent temporal relationships. Recognition rates of 100% are reported for both approaches using a single speaker, but the neural network model achieves consistently higher rates for the multiple speakers obtaining a highest recognition rate of 92.5%.