Abstract
This paper describes a novel speech analysis method that creates a readable pattern based on locally linear embedding (LLE). LLE is an unsupervised learning algorithm for feature extraction. If the speech variability is described by a small number of continuous features, then we can imagine the data as lying on a low dimensional manifold in the high dimensional space of speech waveforms. The goal of feature extraction is to reduce the dimensionality of the speech signal while preserving the informative signatures. In this paper we have present results from the analysis of speech data using PCA and LLE. And we observed that the nonlinear embeddings of LLE separated certain Chinese phonemes better than the linear projections of PCA.
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