Robust Feature Extraction for Speaker Recognition Based on Constrained Nonnegative Tensor Factorization |
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Authors: | Qiang Wu Li-Qing Zhang Guang-Chuan Shi |
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Affiliation: | 1.Department of Computer Science and Engineering,Shanghai Jiao Tong University,Shanghai,China |
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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. |
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