Histogram equalization using a reduced feature set of background speakers’ utterances for speaker recognition

Abstract

We propose a method for histogram equalization using supplement sets to improve the performance of speaker recognition when the training and test utterances are very short. The supplement sets are derived using outputs of selection or clustering algorithms from the background speakers’ utterances. The proposed approach is used as a feature normalization method for building histograms when there are insufficient input utterance samples. In addition, the proposed method is used as an i-vector normalization method in an i-vector-based probabilistic linear discriminant analysis (PLDA) system, which is the current state-of-the-art for speaker verification. The ranks of sample values for histogram equalization are estimated in ascending order from both the input utterances and the supplement set. New ranks are obtained by computing the sum of different kinds of ranks. Subsequently, the proposed method determines the cumulative distribution function of the test utterance using the newly defined ranks. The proposed method is compared with conventional feature normalization methods, such as cepstral mean normalization (CMN), cepstral mean and variance normalization (MVN), histogram equalization (HEQ), and the European Telecommunications Standards Institute (ETSI) advanced front-end methods. In addition, performance is compared for a case in which the greedy selection algorithm is used with fuzzyC-means and K-means algorithms. The YOHO and Electronics and Telecommunications Research Institute (ETRI) databases are used in an evaluation in the feature space. The test sets are simulated by the Opus VoIP codec. We also use the 2008 National Institute of Standards and Technology (NIST) speaker recognition evaluation (SRE) corpus for the i-vector system. The results of the experimental evaluation demonstrate that the average system performance is improved when the proposed method is used, compared to the conventional feature normalization methods.

Keywords

Speaker recognition / Histogram equalization / i-vector

References

[1] Atal,B.S., 1974. Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification. J. Acoust. Soc. Am., 55(6):1304–1312. http://dx.doi.org/10.1121/1.1914702

[2] Blanco,Y., Zazo,S., Principe,J.C. , 2000. Alternative statistical Gaussianity measure using the cumulative density function. Proc. 2nd Int. Workshop on Independent Component Analysis and Blind Signal Separation, p.537–542.

[3] Bousquet,P., Matrouf, D., Bonastre,J. , 2011. Intersession compensation and scoring methods in the i-vectors space for speaker recognition. INTERSPEECH, p.485–488.

[4] Bousquet,P., Larcher, A., Matrouf,D. , , 2012. Variance-spectra based normalization for i-vector standard and probabilistic linear discriminant analysis. Odyssey: the Speaker and Language Recognition Workshop, p.157–164.

[5] Cannon,R.L., Dave,J.V., Bezdek,J.C. , 1986. Efficient implementation of the fuzzy c-means clustering algorithms. IEEE Trans. Patt. Anal. Mach. Intell., 8(2):248–255. http://dx.doi.org/10.1109/TPAMI.1986.4767778

[6] Dehak,N., Kenny, P., Dehak,R. , , 2011. Front-end factor analysis for speaker verification. IEEE Trans. Audio Speech Lang. Process., 19(4):788–798. http://dx.doi.org/10.1109/TASL.2010.2064307

[7] de la Torre,A., Peinado, A.M., Segura,J.C. , , 2005. Histogram equalization of speech representation for robust speech recognition. IEEE Trans. Audio Speech Lang. Process., 13(3):355–366. http://dx.doi.org/10.1109/TSA.2005.845805

[8] Duda,R.O., Hart,P.E., Stork,D.G. , 2012. Pattern Classification. John Wiley & Sons, Tronto.

[9] ETSI, 2005. Transmission and Quality Aspects (STQ); Distributed Speech Recognition; Extended Advanced Front-End Feature Extraction Algorithm; Compression Algorithms; Back-End Speech Reconstruction Algorithm, ETSI ES 202 212. European Telecommunication Standards Institute, Sophia Antipolis.

[10] Franc,V., 2005. Optimization Algorithms for Kernel Methods. PhD Thesis, Centre for Machine Perception, Czech Technical University, Prague, Czech Republic.

[11] Garcia-Romero,D., Espy-Wilson, C.Y., 2011. Analysis of i-vector length normalization in speaker recognition systems. INTERSPEECH, p.249–252.

[12] Gonzalez,R.C., Wintz, P., 1987. Digital Image Processing. Addision-Wesley Publishing Company, Boston.

[13] Jiang,Y., Lee,K., Tang,Z., , 2012. PLDA modeling in i-vector and supervector space for speaker verification. INTERSPEECH, p.1680–1683.

[14] Jones,E., Oliphant, T., Peterson,P. , 2001. Scipy: Open Source Scientific Tools for Python. http://www.scipy.org/

[15] Kenny,P., 2010. Bayesian speaker verification with heavytailed priors. Odyssey: the Speaker and Language Recognition Workshop.

[16] Kim,M., Yang,I., Yu,H., 2008. Robust speaker identification using greedy kernel PCA. 20th IEEE Int. Conf. on Tools with Artificial Intelligence, p.143–146. http://dx.doi.org/10.1109/ICTAI.2008.105

[17] Kim,N., 1998. Statistical linear approximation for environment compensation. IEEE Signal Process. Lett., 5(1):8–10. http://dx.doi.org/10.1109/97.654866

[18] Larcher,A., Bonastre, J., Fauve,B. , , 2013. Alize 3.0—open source toolkit for state-of-the-art speaker recognition. INTERSPEECH, p.2768–2772.

[19] Moreno,P.J., Raj,B., Stern,R.M. , 1996. A vector Taylor series approach for environment-independent speech recognition. Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, p.733–736. http://dx.doi.org/10.1109/ICASSP.1996.543225

[20] Pelecanos,J., Sridharan, S., 2001. Feature warping for robust speaker verification. Odyssey: the Speaker and Language Recognition Workshop, p.213–218.

[21] Reynolds,D.A., Quatieri, T.F., Dunn,R.B. , 2000. Speaker verification using adapted Gaussian mixture models. Dig. Signal Process., 10(1):19–41. http://dx.doi.org/10.1006/dspr.1999.0361

[22] Segura,J.C., Benítez, C., de la Torre,A. , , 2004. Cepstral domain segmental nonlinear feature transformations for robust speech recognition. IEEE Signal Process. Lett., 11(5):517–520. http://dx.doi.org/10.1109/LSP.2004.826648

[23] Skosan,M., Mashao, D., 2006. Modified segmental histogram equalization for robust speaker verification. Patt. Recog. Lett., 27(5):479–486. http://dx.doi.org/10.1016/j.patrec.2005.09.009

[24] Stolcke,A., Kajarekar, S., Ferrer,L. , 2008. Nonparametric feature normalization for SVM-based speaker verification. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, p.1577–1580.

[25] Valin,J.M., Vos,K., Terriberry,T. , 2012. Definition of the Opus Audio Codec. http://opus-codec.org/

[26] Viikki,O., Laurila, K., 1998. Cepstral domain segmental feature vector normalization for noise robust speech recognition. Speech Commun., 25(1):133–147. http://dx.doi.org/10.1016/S0167-6393(98)00033-8

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