Speaker Recognition Using Dynamic Time Warping Polynomial Kernel SVM with Confusion Matrix

Piyush Mishra*, Piyush Lotia**
* Department of Electronics and Telecommunication Engineering, Shri Shankaracharya College of Engineering and Technology, Bhilai, India.
** Associate Professor & HOD, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya College of Engineering and Technology, Bhilai, India.
Periodicity:September - November'2015
DOI : https://doi.org/10.26634/jcom.3.3.3662

Abstract

In this paper, the authors have presented an efficient algorithm for improving the performance of speaker verification system by using polynomial kernel support vector machine along with dynamic time warping. The objective of speaker verification is to verify the identity of the speaker by characterizing the information of speaker. The idea is to improve the accuracy of Support Vector Machine (SVM) classifier with the combination of dynamic time warping and polynomial kernel. The resultant of SVM has higher degree of precision as well as accuracy. To characterize the classification accuracy and precision, we use a technique called as confusion matrix. The authors have performed the experiment over database of 30 speakers including male and female voices. The polynomial kernel SVM is used here to improve the accuracy.

Keywords

Polynomial Kernel, Dynamic Time Warping, Confusion Matrix, Support Vector Machine (SVM), Classifier, Accuracy, Precision.

How to Cite this Article?

Mishra, P., Lotia, P. (2015). Speaker Recognition Using Dynamic Time Warping Polynomial Kernel SVM with Confusion Matrix. i-manager’s Journal on Computer Science, 3(3), 23-27. https://doi.org/10.26634/jcom.3.3.3662

References

[1]. C. J. C. Burges, (1998). “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery, Vol.2, No.2, pp.1–47.
[2]. V. Wan, J. Carmichael, (2005). “Polynomial Dynamic Time Warping Kernel Support Vector Machines for Dysarthric Speech Recognition with Sparse Training Data”, th 9 European Conference on Speech Communication and Technology, pp.3321-3324.
[3]. V. Mohan Patro, Manas Ranjan Patra, (2014). “Augmenting Weighted Average with Confusion Matrix to Enhance Classification Accuracy”, TMLAI, Vol.2, issue 4 ISSN: 2054-7390.
[4]. V. Wan, (2003). “Speaker Verification using Support Vector Ma chines”, Ph.D. thesis, University of Sheffield.
[5]. V. Wan and S. Renals, (2005). “Speaker Verification using Sequence Discriminant Support Vector Machines,” IEEE Transactions on Speech and Audio Proceedings, Vol.13, No.2, pp.203-210.
[6]. B. Scholkopf, C. Burges, and A. Smola, Eds., (1999). Advances in Kernel Methods” - Support Vector Learning, MIT Press.
[7]. L. Rabiner and B. H. Juang, (1993). Fundamentals of Speech Recognition, Prentice Hall.
[8]. Najim Dehak, and Patrick Kenny, (2009). “Support Vector Machines versus Fast Scoring in the Low- Dimensional Total Variability Space for Speaker th Verification”, 10 Annual Conference of the International Speech Communication Association, pp.1559-1562.
[9]. Shantanu Godbole and Sunita Sarawagi, (2004). “Discriminative Methods for Multi-labeled Classification”, Springer-Verlag Berlin Heidelberg, pp.22-30.
[10]. S. Fine, J. Navratil, and R. A. Gopinath, (2001). “A Hybrid GMM/SVM Approach To Speaker Identification,” Proceedings of ICASSP, Vol.1, pp.417–420.
[11]. V. Wan and S. Renals, (2002). “Evaluation of Kernel Methods For Speaker Verification And Identification,” Proceedings of ICASSP, Vol.1, pp.669 672.
[12]. C. Watkins, (1999). “Dynamic Alignment Kernels,” th 15 Annual Conference of the International Speech Communication Association.
[13]. W. M. Campbell, (2002). “Generalised Linear Disciminant Sequence Kernels For Speaker Recognition,” Proceedings of ICASSP.
[14]. Svetlana Segrceanu, and Tiberius zaharia, (2013). “Speaker Verification Using The Dynamic Time Warping” U.P.B. Sci. Bull., Series C, Vol.75, No.1.
[15]. KarthikaVijayan, Vinay Kumar and K. Sri Rama Murty, (2014). “Feature Extraction from Analytic Phase of Speech Signals for Speaker Verification”, INTERSPEECH, pp.1658- 1662.
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