Journal of Computer Science and Technology

, Volume 25, Issue 4, pp 783–792

Robust Feature Extraction for Speaker Recognition Based on Constrained Nonnegative Tensor Factorization

Regular Paper

DOI: 10.1007/s11390-010-9365-6

Cite this article as:
Wu, Q., Zhang, LQ. & Shi, GC. J. Comput. Sci. Technol. (2010) 25: 783. doi:10.1007/s11390-010-9365-6

Abstract

How to extract robust feature is an important research topic in machine learning community. In this paper, we investigate robust feature extraction for speech signal based on tensor structure and develop a new method called constrained Nonnegative Tensor Factorization (cNTF). A novel feature extraction framework based on the cortical representation in primary auditory cortex (A1) is proposed for robust speaker recognition. Motivated by the neural firing rates model in A1, the speech signal first is represented as a general higher order tensor. cNTF is used to learn the basis functions from multiple interrelated feature subspaces and find a robust sparse representation for speech signal. Computer simulations are given to evaluate the performance of our method and comparisons with existing speaker recognition methods are also provided. The experimental results demonstrate that the proposed method achieves higher recognition accuracy in noisy environment.

Keywords

pattern recognition speaker recognition nonnegative tensor factorization feature extraction auditory perception 

Copyright information

© Springer 2010

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina

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