[1] REYNOLDS D A. An overview of automatic speaker recognition technology[C]//Proceedings of the 2002 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway, NJ: IEEE, 2002: IV-4072-IV-4075. [2] ATAL B S. Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification[J]. Journal of the Acoustical Society of America, 1974, 55(6): 1304-1322. [3] CAMPBELL J P. Speaker recognition: a tutorial[J]. Proceedings of the IEEE, 1997, 85(9): 1437-1462. [4] ATAL B S, HANAUER S L. Speech analysis and synthesis by linear prediction of speech wave[J]. Journal of the Acoustical Society of America, 1971, 50(2): 637-655. [5] JING X X, MA J L, ZHAO J, et al. Speaker recognition based on principal component analysis of LPCC and MFCC[C]//Proceedings of the 2014 IEEE International Conference on Signal Processing, Communications and Computing. Piscataway, NJ: IEEE, 2014: 403-408. [6] DEHAK N, KENNY P J, DEHAK R, et al. Front-end factor analysis for speaker verification[J]. IEEE Transactions on Acoustics, Speech and Signal Processing, 2011, 19(4): 788-798. [7] DAVIS S B, MERMELSTEIN P. Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences[J]. IEEE Transactions on Acoustics, Speech and Signal Processing, 1980, 28(4): 357-366. [8] MUBARAK O M, AMBIKAIRAJAH E, EPPS J. Analysis of an MFCC-based audio indexing system for efficient coding of multimedia sources[C]//Proceedings of the 8th International Symposium on Signal Processing and Its Applications. Piscataway, NJ: IEEE, 2005: 619-622. [9] NIST. The NIST speaker recognition evaluation[EB/OL].[2015-10-10]. http://www.itl.nist.gov/iad/mig//tests/sre/2010/index.html. [10] NGHIA P T, BINH P V, THAI N H, et al. A robust wavelet-based text-independent speaker identification[C]//Proceedings of the 2007 International Conference on Computational Intelligence and Multimedia Applications. Piscataway, NJ: IEEE, 2007: 219-223. [11] KUMAR P, CHANDRA M. Hybrid of wavelet and MFCC features for speaker verification[C]//Proceedings of the 2011 World Congress on Information and Communication Technologies. Piscataway, NJ: IEEE, 2011: 1150-1154. [12] AI O C, HARIHARAN M, YAACOB S, et al. Classification of speech dysfluencies with MFCC and LPCC features[J]. Expert Systems with Applications, 2012, 39(2): 2157-2165. [13] YUAN Y J, ZHAO P H, ZHOU Q. Research of speaker recognition based on combination of LPCC and MFCC[C]//Proceedings of the 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems. Piscataway, NJ: IEEE, 2010: 765-767. [14] ZHOU H L, WANG J, WANG M C, et al. Amplitude spectrum compensation and phase spectrum correction of seismic data based on the generalized S transform[J]. Applied Geophysics, 2014,11(4): 468-478. [15] HUANG N T, ZHANG S X, CAI G W, et al. Power quality disturbances recognition based on a multiresolution generalized S-transform and a PSO-improved decision tree[J]. Energies, 2015, 8(1): 549-572. [16] HARIHARAN M, VIJEAN V, SINDHU R, et al. Classification of mental tasks using Stockwell transform[J]. Computers and Electrical Engineering, 2014, 40(5): 1741-1749. [17] MOUKADEM A, DIETERLEN A, HUEBER N, et al. A robust heart sounds segmentation module based on S-transform[J]. Biomedical Signal Processing and Control, 2012, 8(3): 273-281. [18] YIN B Q, HE Y G, WU X M. A method for magnetocardiograms filtering based on singular value decomposition and S-transform[J]. Acta Physica Sinica, 2013, 62(14): 148702. [19] GUO Y J, WEI Y D, ZHOU X J, et al. Impact feature extracting method based on S transform time-frequency spectrum denoised by SVD[J]. Journal of Vibration Engineering, 2014, 27(4): 621-628. [20] STOCKWELL R G, MANSINHA L, LOWE R P. Localization of the complex spectrum: the S transform[J]. IEEE Transactions on Signal Processing, 1996, 44(4): 998-1001. [21] STOCKWELL R G. Why use the S-transform?[EB/OL].[2015-10-17]. https://bytebucket.org/cleemesser/stockwelltransform/raw/d87ff20d787d36d5280dcd26cbaf309dcd982bf4/ref/Stockwell-Why%20Use%20the%20S-Transform.pdf [22] CONG F Y, ZHONG W, TONG S G, et al. Research of singular value decomposition based on slip matrix for rolling bearing fault diagnosis[J]. Journal of Sound and Vibration, 2015, 344: 447-463. [23] YANG W X, TSE P W. Development of an advanced noise reduction method for vibration analysis based on singular value decomposition[J]. NDT&E International, 2003, 36(6): 419-432. [24] JANKOWSKI C, KALYANSWAMY A, BASSON S, et al. NTIMIT: a phonetically balanced, continuous speech, telephone bandwidth speech database[C]//Proceedings of the 1990 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway, NJ: IEEE, 1990: 109-112. [25] SADJADI S O, SLANEY M, HECK L. MSR Identity Toolbox v1.0: a Matlab toolbox for speaker recognition research[EB/OL].[2015-10-17]. http://research.microsoft.com/en-us/downloads/2476c44a-1f63-4fe0-b805-8c2de395bb2c/. [26] LI Q, HUANG Y. An auditory-based feature extraction algorithm for robust speaker identification under mismatched conditions[J]. IEEE Transaction on Audio, Speech, and Language Processing, 2011, 19(6): 1791-1801. [27] LI Q, HUANG Y. Robust speaker identification using an auditory-based feature[C]//Proceedings of the 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway, NJ: IEEE, 2010: 4514-4517. [28] 李作强,高勇.基于CFCC和相位信息的鲁棒性说话人辨识[J].计算机工程与应用,2015,51(17):228-232.(LI Z Q, GAO Y. Robust speaker identification based on CFCC and phase information[J]. Computer Engineering and Applications, 2015, 51(17): 228-232.) |