Abstract
The paper presents a Segment-Mean method for reducing the dimension of the speech feature parameters. K-Means function is used to group the speech feature parameters whose dimension has been reduced. And then the speech samples are classified into different clusters according to their features. It proposes a cross-group training algorithm for the speech feature parameters clustering which improves the accuracy of the clustering function. When recognizing speech, the system uses a cross-group HMM models algorithm to match patterns which reduces the calculation by more than 50% and without reducing the recognition rate of the small vocabulary speech recognition system.
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