Abstract
A discrete wavelet transform algorithm segregates the operand data set sequentially. It generates computational intermediates which represent it at graded resolutions and leads to a reciprocal domain within which information is multiply resolved in terms of the time-frequency localization of the component wavelet basis vectors. Based on this we introduce the concept of wavelet subtransform domains and show that these can be used to selectively enhance acoustic events in a speech signal. Enhancement of phoneme classes improves segmentation and recognition performance for reasons that we have pointed out in the paper. As an experimental verification, we use subtransform domains to design a preprocessor for a Hindi database and evaluate the subsequent recognition performances using hidden Markov models and two standard parametrizations.
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