Abstract
When there is a mismatch between the acoustic training environment and the testing environment, the performance of automatic speaker identification systems degrades significantly. A robust feature extraction method for speaker recognition based on the gammatone filter is therefore proposed in this paper. By employing the working mechanism of the human auditory model instead of the traditional triangular filter banks, gammatone filter banks are used to simulate the auditory model of the human ear cochlea. The cube root compression method, equal loudness technology, and relative spectral (RASTA) filtering technology are incorporated into the robust feature extraction process. A simulation experiment is conducted based on the Gaussian mixture model (GMM) recognition algorithm. The experimental results indicate that the proposed feature parameters could show superior robustness and represent the characteristics of the speaker better than the conventional mel-frequency cepstrum coefficient (MFCC), cochlear cepstrum coefficient (CFCC) and relative spectra-perceptual linear predictive (RASTA-PLP) parameters.
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The authors would like to give an acknowledgment to authors of references and a great deal of work done by them, as well as the co-workers for their helpful comments. The authors would like to thank anonymous reviewers for their useful comments that help revising the paper.
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Li, Z., Gao, Y. Acoustic feature extraction method for robust speaker identification. Multimed Tools Appl 75, 7391–7406 (2016). https://doi.org/10.1007/s11042-015-2660-z
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DOI: https://doi.org/10.1007/s11042-015-2660-z