Abstract
This paper presents to ameliorate the performance of text-independent speaker recognition system in a noisy environment and cross-channel recordings of the utterances. In this paper presents the combination of Gammatone Frequency Cepstral Coefficients (GFCC) to handle noisy environment with i-vectors to handle the session variability. Experiments are evaluated on NIST-2003 database.
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References
D. A. Reynolds, “Experimental Evaluation of Features for Robust Speaker Identification,” IEEE Trans. on Acoustic Speech and Audio Processing, vol. 2, no. 4, 1994.
Davis, S. Mermelstein, P.,” Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Sentences”, In IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 28 No. 4, pp. 357–366, 1980.
H. Seddik, A. Rahmouni and M. Sayadi, “Text independent speaker recognition using the mel frequency cepstral coefficients and a neural network classifier”, Ecole Nationale Des Sciences Informatiques, 2010, Manouba, Tunisia.
R.D Patterson, I. Nimmo-Smith, J. Holdsworth, and P. Rice, “An efficient auditory filterbank based on Gammatone function,” in Paper presented at a meeting of the IOC Speech Group on Auditory Modelling at RSRE, December 14–15, 1987.
N. Dehak, R. Dehak, P. Kenny, N. Brummer, P. Ouellet, and P. Dumouchel, “Support vector machines versus fast scoring in the low-dimensional total variability space for speaker verification,” in Proceedings of Inter speech, Brighton, UK, 2009.
T. Kinnunen and P. Rajan. “A practical, self-adaptive voice activity detector for speaker verification with noisy telephone and microphone data,” in Proc. International Conference on Acoustics, Speech, and Signal Processing, 2013, pp. 7229–7233.
R. Patterson and I. N. Smith, “An efficient auditory filter bank based on the gammatone function,” Speech-Group meeting of the Institute of Acoustics on Auditory Modelling, vol. 54, Apr 1987FLEXChip Signal Processor (MC68175/D), Motorola, 1996.
D. Povey, S.M. Chu, B. Varadarajan, Universal background model based speech recognition. IEEE International Conference on Acoustics, Speech and Signal Processing, 2008.
M. Przybocki, A. Martin, and A. Le, “NIST speaker recognition evaluations utilizing the mixer corpora 2004, 2005, 2006,” IEEE Trans. Audio, Speech, Lang. Process., vol. 15, no. 7, pp. 1951–1959, Sep. 2007.
Zhao, Xiaojia et Wang, DeLiang. Analyzing noise robustness of MFCC and GFCC features in speaker identification. In: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. IEEE, 2013. p. 7204–7208.
Acknowledgment
The present study is a part of ongoing project on “Personal Authentication using Multimodal Behavioural Biometrics: Voice and Gait” and the authors express their gratitude to the Department of Science & Technology, Govt. Of India for funding the project.
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Jeevan, M., Dhingra, A., Hanmandlu, M., Panigrahi, B.K. (2017). Robust Speaker Verification Using GFCC Based i-Vectors. In: Lobiyal, D., Mohapatra, D., Nagar, A., Sahoo, M. (eds) Proceedings of the International Conference on Signal, Networks, Computing, and Systems. Lecture Notes in Electrical Engineering, vol 395. Springer, New Delhi. https://doi.org/10.1007/978-81-322-3592-7_9
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DOI: https://doi.org/10.1007/978-81-322-3592-7_9
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