Improvements in Neural-Network Training and Search Techniques for Continuous Digit Recognition

This paper describes a set of experiments on training and search techniques for development of a neural-network based continuous digits recognizer. When the best techniques from these experiments were combined to train a final recognizer, there was a 56% reduction in word-level error on the continuous digits recognition task. The best system had word accuracy of 97.67% on a test set of the OGI 30K Numbers corpus; this corpus contains naturally-produced continuous digit strings recorded over telephone channels. Experiments investigated the effects of the feature set, the amount of data used for training, the type of context-dependent categories to be recognized, the values for duration limits, and the type of grammar. The experiments indicate that the grammar and duration limits had a greater effect on recognition accuracy than the output categories, cepstral features, or a doubling of the amount of training data. In addition, the forwardbackward method of training neural networks was employed in developing the final network.