Title: An Improved Union Model for Continuous Speech Recognition with Partial Duration Corruption

Authors: Ming Ji

Abstract: The probabilistic union model is improved for continuous speech recognition involving partial duration corruption, assuming no knowledge about the corrupting noise. The new developments include: an n-best rescoring strategy for union based continuous speech recognition, a dynamic segmentation algorithm for reducing the number of corrupted segments in the union model, and a combination of the union model with conventional noise-reduction techniques to accommodate the mixtures of stationary noise (e.g. car) and random, abrupt noise (e.g. a car horn). The proposed system has been tested for connected-digit recognition, subjected to various types of noise with unknown, time-varying characteristics. The results have shown significant robustness for the new model.

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