An associative memory based on an electronic neural network architecture

Abstract
A statistical approach for the segmentation of a continuous speech signal to detect acoustic events is presented. Experiments are carried out to test the segmentation algorithms. Reasonable results are obtained with speech signals, although these are not exactly piecewise stationary. A comparison between the experimental results of automatic and handmade segmentations, demonstrates the potential acoustic-phonetic classification capability of the proposed algorithms.<>

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