NDLI: Speech analysis based on locally linear embedding(LLE)

Content Provider	IEEE Xplore Digital Library
Author	Lifang Xue Tingjun Qian
Copyright Year	2010
Description	Author affiliation: Software College, Northeastern University, Shenyang Liaoning, CHINA (Tingjun Qian) \|\| Computing Center, Northeastern University, Shenyang Liaoning, CHINA (Lifang Xue)
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.
Starting Page	2159
Ending Page	2162
File Size	353528
Page Count	4
File Format	PDF
ISBN	9781424459582
e-ISBN	9781424459612
DOI	10.1109/ICNC.2010.5584263
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Manifolds Locally linear embedding (LLE) Speech analysis Data visualization Speech Principal component analysis Nearest neighbor searches speech analysis
Content Type	Text
Resource Type	Article

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