Statistical feature of pitch frequency distributions for robust speaker identification |
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Authors: | Linghua Zhang Baoyu Zheng Zhen Yang |
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Institution: | Dept of Info. Eng., Nanjing University of Posts & Telecom., Nanjing 210003, China |
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Abstract: | This letter proposes an effective and robust speech feature extraction method based on statistical analysis of Pitch Frequency
Distributions (PFD) for speaker identification. Compared with the conventional cepstrum, PFD is relatively insensitive to
Additive White Gaussian Noise (AWGN), but it does not show good performance for speaker identification, even if under clean
environments. To compensate this shortcoming, PFD and conventional cepstrum are combined to make the ultimate decision, instead
of simple taking one kind of features into account Experimental results indicate that the hybrid approach can give outstanding
improvement for text-independent speaker identification under noisy environments corrupted by AWGN.
Communication author: Zhang Linghua, born in 1964, female, associate professor. Department of Information Engineering, Nanjing
University of Posts & Telecommunications, Nanjing 210003, China. |
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Keywords: | Speaker identification Feature extraction Pitch frequency Gaussian Mixture Model (GMM) |
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