Abstract
Speaker recognition systems have shown good performance in noise-free environments, but the performance will severely deteriorate in the presence of noises. At the front end of the systems, Mel-Frequency Cepstral Coefficient (MFCC), or a relatively noise-robust feature Gammatone Frequency Cepstral Coefficients (GFCC), is commonly used as time-frequency feature. To further improve the noise-robustness of GFCC, signal processing techniques, such as DC removal, pre-emphasis and Cepstral Mean Variance Normalization (CMVN), are investigated in the extraction of GFCC. Being aware the advantages and disadvantages of MFCC and GFCC, an adaptive strategy was proposed to make feature selection based on the quality of speech. Experiments were conducted on TIMIT dataset to evaluate our approach. Compared with ordinary GFCC and MFCC features, our method significantly reduced the EER in speech data with miscellaneous SNRs.
This work is supported by the Natural Science Foundation of Jiangsu Province for Excellent Young Scholars (BK20180080).
X. Zhang is working for a master degree at the Lab of Intelligent Information Processing of PLA Army Engineering University. His research topic is speaker recognition.
X. Zou is now an associate professor at the Lab of Intelligent Information Processing of PLA Army Engineering University. His research interest is speech signal processing.
M. Sun is now a researcher at the Lab of Intelligent Information Processing of PLA Army Engineering University. His research interests are speech processing, unsupervised/semi-supervised machine learning and sequential pattern recognition.
P. Wu is working for a master degree at the Lab of Intelligent Information Processing of PLA Army Engineering University. His research topic is speech signal processing.
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Zhang, X., Zou, X., Sun, M., Wu, P. (2020). Robust Speaker Recognition Using Improved GFCC and Adaptive Feature Selection. In: Yang, CN., Peng, SL., Jain, L. (eds) Security with Intelligent Computing and Big-data Services. SICBS 2018. Advances in Intelligent Systems and Computing, vol 895. Springer, Cham. https://doi.org/10.1007/978-3-030-16946-6_13
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DOI: https://doi.org/10.1007/978-3-030-16946-6_13
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