Speaker verification based on SVM and total variability

doi:10.16511/j.cnki.qhdxxb.2017.26.003

Abstract
Figures/Tables
References
Related Articles
Metrics

Download: PDF(1117 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract The total variability factor extractor and the probability linear discriminant analysis (PLDA) algorithms have been the state-of-the-art for text-independent speaker verification. This study combines a support vector machine (SVM) with the PLDA. The low dimensional i-vectors of the total variability system are used as the inputs to the support vector machine, with the cosine kernel function used to achieve better discrimination. This method achieves considerable performance improvement with the PLDA system. Furthermore, the score fusion of the SVM with the PLDA give even better results. Tests were conducted on the female part of the interview section of the NIST 2012 core test corpus. The detection cost function (DCF) of the fusion system was reduced by 25.1% for common condition 1 and 25.2% for condition 3 compared with the best results for a single system.

Keywords speaker verification total variability support vector machine kernel function

ZTFLH:

TN912.34

Issue Date: 15 March 2017

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	GUO Wu
	ZHANG Sheng
	XU Jie
	HU Guoping
	MA Xiaokong

Cite this article:

GUO Wu,ZHANG Sheng,XU Jie, et al. Speaker verification based on SVM and total variability[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(3): 240-243.

URL:

http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2017.26.003 OR http://jst.tsinghuajournals.com/EN/Y2017/V57/I3/240

[1]	Reynolds D A, Quatieri T F, Dunn R B. Speaker verification using adapted Gaussian mixture models[J]. Digital Signal Processing, 2000, 10(1):19-41.
[2]	Kenny P, Boulianne G, Ouellet P, et al. Joint factor analysis versus eigenchannels in speaker recognition[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2007, 15(4):1435-1447.
[3]	Dehak N, Kenny P J, Dehak R, et al. Front-end factor analysis for speaker verification[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2011, 19(4):788-798.
[4]	Prince S J D, Elder J H. Probabilistic linear discriminant analysis for inferences about identity[C]//2007 IEEE 11th International Conference on Computer Vision. Rio de Janeiro, Brazil:IEEE Press, 2007:1-8.
[5]	Burget L, Plchot O, Cumani S, et al. Discriminatively trained probabilistic linear discriminant analysis for speaker verification[C]//2011 IEEE international conference on acoustics, speech and signal processing (ICASSP). Prague, Czech Republic:IEEE Press, 2011:4832-4835.
[6]	Jiang Y, Kong A L, Wang L. PLDA in the i-supervector space for text-independent speaker verification[J]. Eurasip Journal on Audio Speech and Music Processing, 2014, 2014(1):1-13.
[7]	Kenny P, Stafylakis T, Ouellet P, et al. PLDA for speaker verification with utterances of arbitrary duration[C]//2013 IEEE International Conference on Acoustics, Speech and Signal Processing. Brisbane, Australia:IEEE Press, 2013:7649-7653.
[8]	Li N, Mak M W. SNR-invariant PLDA modeling in nonparametric subspace for robust speaker verification[J]. IEEE/ACM Transactions on Audio Speech and Language Processing, 2015, 23(10):1648-1659.
[9]	Bourouba H, Korba C A, Djemili R. Novel approach in speaker identification using SVM and GMM[J]. Control Engineering & Applied Informatics, 2013, 15(3):87-95. url: http://dx.doi.org/ol Engineering
[10]	Ding I J, Yen C T, Ou D C. A method to integrate GMM, SVM and DTW for speaker recognition[J]. International Journal of Engineering and Technology Innovation, 2014, 4(1):38-47.
[11]	Campbell W M, Sturim D E, Reynolds D A, et al. SVM based speaker verification using a GMM supervector kernel and NAP variability compensation[C]//2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings. Brisbane, Australia:IEEE Press, 2006, 1:I-I.
[12]	Solomonoff A, Quillen C, Campbell W M. Channel compensation for SVM speaker recognition[C]//ICASSP 2005, Acoustics, Speech, and Signal Processing Proceedings. Philadelphia, PA, USA:IEEE Press, 2010:629-632."

[1]	TU Shouzhong, YANG Jing, ZHAO Lin, ZHU Xiaoyan. Filtering Chinese microblog topics noise algorithm based on a semi-supervised model[J]. Journal of Tsinghua University(Science and Technology), 2019, 59(3): 178-185.
[2]	KARI·Tusongjiang, GAO Wensheng, ZHANG Ziwei, MO Wenxiong, WANG Hongbing, CUI Yiping. Power transformer fault diagnosis based on a support vector machine and a genetic algorithm[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(7): 623-629.
[3]	CHEN Dongqing, ZHANG Puhan, WANG Huazhong. Intrusion detection for industrial control systems based on an improved SVM method[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(4): 380-386.
[4]	XU Hongping, LIU Yang, YI Hang, YAN Xiaotao, KANG Jian, ZHANG Wenjin. Abnormal traffic flow identification for a measurement and control network for launch vehicles[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(1): 20-26,34.
[5]	LIU Chengying, WU Hao, WANG Liping, ZHANG Zhi. Tool wear state recognition based on LS-SVM with the PSO algorithm[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(9): 975-979.
[6]	IMAM Seyyare, PARHAT Rayilam, HAMDULLA Askar, LI Zhijun. Keyword extraction algorithms for emotion recognition from Uyghur text[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(3): 270-273.
[7]	XIN Zhe, ZOU Ruobing, LI Shengbo, YU Jiaying, DAI Yifan, CHEN Hailiang. Target recognition around a vehicle based on an ultrasonic sensor array[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(12): 1287-1295.
[8]	YANG Diange, HE Changwei, LI Man, HE Qiguang. Vehicle steering and lane-changing behavior recognition based on a support vector machine[J]. Journal of Tsinghua University(Science and Technology), 2015, 55(10): 1093-1097.
[9]	Chao ZHANG, Yi LIU, Hui ZHANG, Hong HUANG. Study on urban short-term gas load forecasting based on support vector machine model[J]. Journal of Tsinghua University(Science and Technology), 2014, 54(3): 320-325.

Viewed

Full text

Abstract

Cited

Shared

Discussed