Local Linear Wavelet Neural Network and RLS for Usable Speech Classification
While operating in a co-channel environment, the accuracy of the
speech processing technique degrades. When more than one person is
talking at same time, then there occurs the co-channel speech. The
objective of usable speech segmentation is identification and
extraction of those portions of co-channel speech that are degraded in
a negligible range but still needed for various speech processing
application like speaker identification. Some features like usable
speech measures are extracted from the co-channel signal to
differentiate between usable and unusable types of speech. The
features are extracted recursively by this new method and variable
length segmentation is carried out by making sequential decision on
class assignment of LLWNN pattern classifier. The correct
classification using this technique is 84.5% whereas the false
classification is 15.5%. The result shows that the proposed classifier
gives better classification and is robust.
Keywords: Co-channel Speech; Usable Speech, Sequential Detection; LLWNN; RLS; Speaker Identification
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