Phoneme recognition in continuous speech using feature selection based on mutual information
نویسندگان
چکیده
This paper describes an optimal statistical method to recognize phonemes in continuous speech. The novelty of this method is to search the most effective acoustic features in each acoustic Ievel using the criterion of mutual information between acoustic feature vectors and phoneme Iabels assigned to the speech wave. In the proposed method for phoneme recognition using multiple acoustic features, input speech is first dassified based on acoustic similarity, and possible phoneme is selected using variable acoustic features hierarchically. On each Ievel of acoustic features induding power and its variational pattern, LPC Mel-cepstrum aRd its pattern of temporal change are precisely evaluated. Multi-level dustering is suitable to discriminate phonemes by detecting the most reliable features in that context and by using the effective combination of various acoustic characteristics.
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