By Topic

Speech Feature Analysis Using Temporal Linear Embedding

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Lifang Xue ; Comput. Center, Northeastern Univ., Shenyang, China ; XiusHuang Yi

This paper describes a novel speech feature analysis method that creates a readable pattern based on temporal linear embedding. 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. According to LLE, Temporal linear embedding introduces a time enhanced mechanism to improve system performance. The method remedies the defect that LLE ignores temporal factor which is extremely important for speech signal. In this paper we have present results from the analysis of speech data using PCA and temporal linear embedding. And we observed that the nonlinear embeddings of temporal linear embedding separated certain Chinese phonemes better than the linear projections of PCA.

Published in:

Computational Intelligence and Security (CIS), 2011 Seventh International Conference on

Date of Conference:

3-4 Dec. 2011