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: Zero-crossing data can provide important feature information about an utterance which is not available in a purely spectral representation. This report describes the incorporation of zero-crossing information into the spectral representation used in a template-matching system (CICADA). An analysis of zero-crossing data for an extensive (2880 utterance, 8 talker) alpha-digit data base is described. On the basis of this analysis, a zero-crossing algorithm is proposed. The algorithm was evaluated using a confusible subset of the alpha-digit vocabulary (the E-set ). Inclusion of zero-crossing information in the representation leads to a 10-13% reduction in error rate, depending on the spectral representation.

Adding a Zero-Crossing Count to Spectral Information in Template-Based Speech Recognition