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Phoneme recognition using an adaptive supervised manifold learning algorithm

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Abstract

To effectively handle speech data lying on a nonlinear manifold embedded in a high-dimensional acoustic space, in this paper, an adaptive supervised manifold learning algorithm based on locally linear embedding (LLE) for nonlinear dimensionality reduction is proposed to extract the low-dimensional embedded data representations for phoneme recognition. The proposed method aims to make the interclass dissimilarity maximized, while the intraclass dissimilarity minimized in order to promote the discriminating power and generalization ability of the low-dimensional embedded data representations. The performance of the proposed method is compared with five well-known dimensionality reduction methods, i.e., principal component analysis, linear discriminant analysis, isometric mapping (Isomap), LLE as well as the original supervised LLE. Experimental results on three benchmarking speech databases, i.e., the Deterding database, the DARPA TIMIT database, and the ISOLET E-set database, demonstrate that the proposed method obtains promising performance on the phoneme recognition task, outperforming the other used methods.

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Acknowledgments

The authors would like to thank all the anonymous reviewers and editors for their helpful comments and suggestions about the improvement of this paper. This work is supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. Z1101048 and No. Y1111058.

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Authors and Affiliations

  1. School of Physics and Electronic Engineering, Taizhou University, Taizhou, 318000, People’s Republic of China

    Shiqing Zhang

  2. Department of Computer Science, Taizhou University, Taizhou, 318000, People’s Republic of China

    Xiaoming Zhao

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  1. Xiaoming Zhao
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Correspondence to Shiqing Zhang.

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Zhao, X., Zhang, S. Phoneme recognition using an adaptive supervised manifold learning algorithm. Neural Comput & Applic 21, 1501–1515 (2012). https://doi.org/10.1007/s00521-012-1032-0

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