Dimensionality Reduction-Based Phoneme Recognition

摘要：

In this paper,linear and nonlinear dimensionality reduction algorithms are proposed to speech phoneme data from TIMIT corpus in an effort to perform dimensionality reduction for yielding low dimensional features capable of discriminating between phonemes.The linear dimensionality reduction method,including principal component analysis (PCA) and linear discriminant analysis (LDA),and the nonlinear dimensionality reduction method,including locally linear embedding (LLE) and isometric feature mapping (Isomap),are investigated.The resulting features by dimensionality reduction are evaluated in support vector machines (SVM)-based phoneme recognition experiments.Experiment results indicate that traditional linear LDA and PCA techniques for dimensionality reduction are capable of outperforming nonlinear LLE and Isomap techniques for phoneme recognition.

作者: Shiqing Zhang Zhijin Zhao

作者单位: School of Physics and Electronic Engineering,Taizhou University,Taizhou,China School of Telecommunication Engineering,Hangzhou Dianzi University,Hangzhou,China

会议类型: 国际会议

会议名称: 9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-26（万方平台首次上网日期，不代表论文的发表时间）