A Novel Approach for Speaker Recognition by Using Wavelet Analysis and Support Vector Machines

  • Kanaka Durga Returi
  • Vaka Murali Mohan
  • Y. Radhika
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 379)


Speaker recognition approach through wavelet analysis as well as support vector machines is presented in this paper. The wavelet-based approach is used to differentiate among regular and irregular voices. The wavelet filter banks were utilized to coincide by means of support vector machine for extraction of the feature and its classification. This approach creates utilization of wavelets as well as support vector machine to separate particular speech signal through multi-dialog settings. In this approach, first we apply the wavelets to calculate audio features those have sub-band power and calculated pitch values from the given data of the speech. Multi-speaker separation of speech data is carried out by the utilization of SVM more than these audio features as well as other values of the signal. This entire database was utilized to calculate the performance of the system and it represents over 95 % accuracy.


Speaker recognition Wavelet analysis Support vector machines 


  1. 1.
    Truong, T.K., Chien, C.L., ShiHuang, C.: Segmentation of specific speech signals from multi dialog environment using SVM and wavelet. Pattern Recogn. Lett. 28(11), 1307–1313 (2007)CrossRefGoogle Scholar
  2. 2.
    Zhang, X., Liu, X., Wang, Z.J.: Evaluation of a set of new ORF kernel functions of SVM for speech recognition. Eng. Appl. Artif. Intell. 26(10), 2574–2580 (2013)CrossRefGoogle Scholar
  3. 3.
    Fonseca, E.S., Guido, R.C., Maciel, C.D., Pereir, J.C.: Wavelet time-frequency analysis and least squares support vector machines for the identification of voice disorders. Comput. Biol. Med. 37(4), 571–578 (2007)CrossRefGoogle Scholar
  4. 4.
    Sangeeth, J., Jothilakshmi, S.: A novel spoken keyword spotting system using support vector machine. Eng. Appl. Artif. Intell. 36, 287–293 (2014)CrossRefGoogle Scholar
  5. 5.
    Pawan, K.A., Raghunath, S.H.: Fractional Fourier transform based features for speaker recognition using support vector machine. Comput. Electr. Eng. 39(2), 550–557 (2013)CrossRefGoogle Scholar
  6. 6.
    Huang, D.-Y., Zhang, Z., Ge, S.: Speaker state classification based on fusion of asymmetric simple partial least squares (SIMPLS) and support vector machines. Comput. Speech Lang. 28(2), 392–419 (2014)CrossRefGoogle Scholar
  7. 7.
    Wu, J.-D., Lin, B.-F.: Speaker identification using discrete wavelet packet transform technique with irregular decomposition. Expert Syst. Appl. 36(2), 3136–3143 (2009)Google Scholar
  8. 8.
    Francesco, P., Fiore, U., Alfredo, D.S.: On the detection of card-sharing traffic through wavelet analysis and Support Vector Machines. Appl. Soft Comput. 13(1), 615–627 (2013)Google Scholar
  9. 9.
    Arjmandi, M.K., Pooyan, M.: An optimum algorithm in pathological voice quality assessment using wavelet-packet-based features, linear discriminant analysis and support vector machine. Biomed. Signal Process. Control 7(1), 3–19 (2013)CrossRefGoogle Scholar
  10. 10.
    Campbell, W.M., Campbell, J.P., Reynolds, D.A., Singer, E.: Support vector machines for speaker and language recognition. Comput. Speech Lang. 20(2-3), 210–229 (2006)CrossRefGoogle Scholar
  11. 11.
    You, C.H., Li, H.: Relevance factor of maximum a posteriori adaptation for GMM–NAP–SVM in speaker and language recognition. Comput. Speech Lang. 30, 116–134 (2014)Google Scholar
  12. 12.
    Shriberg, E., Ferrer, L., Kajarekar, S., Venkataraman, A.: Modeling prosodic feature sequences for speaker recognition. Speech Commun. 46(3), 455–472 (2005)CrossRefGoogle Scholar
  13. 13.
    Dileep, A.D., Chandra Sekhar, C.: Class-specific GMM based intermediate matching kernel for classification of varying length patterns of long duration speech using support vector machines. Speech Commun. 57, 126–143 (2014)CrossRefGoogle Scholar
  14. 14.
    Zhao, J., Dong, Y., Zhao, X., Yang, H., Liang, L., Wang, H.: Advances in SVM-based system using GMM super vectors for text-independent speaker verification. Tsinghua Sci. Technol. 13(4), 522–527 (2008)CrossRefGoogle Scholar
  15. 15.
    Kinnunen, T., Sidoroff, I., Marko, T., Pasi, F.: Comparison of clustering methods: A case study of text-independent speaker modeling. Pattern Recogn. Lett. 32(13), 1604–1617 (2011)CrossRefGoogle Scholar
  16. 16.
    Kinnunen, T., Li, H.: An overview of text-independent speaker recognition: from features to supervectors. Speech Commun. 52(1), 12–40 (2010)CrossRefGoogle Scholar
  17. 17.
    McLaren, M., Matrouf, D., Vogt, R., Bonastre, J.-F.: Applying SVMs and weight-based factor analysis to unsupervised adaptation for speaker verification. Comput. Speech Lang. 25(2), 327–340 (2011)CrossRefGoogle Scholar
  18. 18.
    Peng, G., Wang, W.S.-Y.: Tone recognition of continuous Cantonese speech based on support vector machines. Speech Commun. 45(1), 49–62 (2005)CrossRefGoogle Scholar
  19. 19.
    Wang, S.-J., Mathew, V., Chen, Y., Lee, J.: Empirical analysis of support vector machine ensemble classifiers. Expert Syst. Appl. 36(3), 6466–6476 (2009)CrossRefGoogle Scholar
  20. 20.
    Hua, S., Sun, Z.: A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. J. Mol. Biol. 308(2, 27), 397–407 (2001)CrossRefGoogle Scholar
  21. 21.
    Gajšek, R., Mihelič, F., Dobrišek, S.: Speaker state recognition using an HMM-based feature extraction method. Comput. Speech Lang. 27(1), 135–150 (2013)CrossRefGoogle Scholar
  22. 22.
    Mohamed, A., Ramachandran Nair, K.N.: HMM/ANN hybrid model for continuous Malayalam speech recognition. Procedia Eng. 30, 616–622 (2012)CrossRefGoogle Scholar
  23. 23.
    Saeedi, N.E., Farshad, A., Farhad, T.: Support vector wavelet adaptation for pathological voice assessment. Comput. Biol. Med. 41(9), 822–828 (2011)CrossRefGoogle Scholar
  24. 24.
    Shih, P.-Y., Lin, P.-C., Wang, J.-F., Lin, Y.-N.: Robust several-speaker speech recognition with highly dependable online speaker adaptation and identification. J. Netw. Comput. Appl. 34(5), 1459–1467 (2011)CrossRefGoogle Scholar
  25. 25.
    Hanilçi, C., Ertaş, F.: Investigation of the effect of data duration and speaker gender on text-independent speaker recognition. Comput. Electr. Eng. 39(2), 441–452 (2013)CrossRefGoogle Scholar
  26. 26.
    Daqrouq, K.: Wavelet entropy and neural network for text-independent speaker identification. Eng. Appl. Artif. Intell. 24(5), 796–802 (2011)CrossRefGoogle Scholar
  27. 27.
    Govindan, S.M., Duraisamy, P., Yuan, X.: Adaptive wavelet shrinkage for noise robust speaker recognition. Digit. Signal Proc. 33, 180–190 (2014)CrossRefGoogle Scholar
  28. 28.
    Rossi, F., Villa, N.: Support vector machine for functional data classification. Neurocomputing 69(7-9), 730–742 (2006)CrossRefGoogle Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  • Kanaka Durga Returi
    • 1
  • Vaka Murali Mohan
    • 2
  • Y. Radhika
    • 1
  1. 1.Department of Computer Science & EngineeringGITAM UniversityVisakhapatnamIndia
  2. 2.Department of Computer Science & EngineeringTRR College of EngineeringHyderabadIndia

Personalised recommendations