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.
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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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Zhang, L., Zheng, B. & Yang, Z. Statistical feature of pitch frequency distributions for robust speaker identification. J. of Electron.(China) 22, 437–442 (2005). https://doi.org/10.1007/BF02687916
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DOI: https://doi.org/10.1007/BF02687916