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2016 | OriginalPaper | Buchkapitel

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

verfasst von : Kanaka Durga Returi, Vaka Murali Mohan, Y. Radhika

Erschienen in: Proceedings of the Second International Conference on Computer and Communication Technologies

Verlag: Springer India

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Abstract

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.

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Literatur
1.
Zurück zum Zitat 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)CrossRef 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)CrossRef
2.
Zurück zum Zitat 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)CrossRef 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)CrossRef
3.
Zurück zum Zitat 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)CrossRef 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)CrossRef
4.
Zurück zum Zitat Sangeeth, J., Jothilakshmi, S.: A novel spoken keyword spotting system using support vector machine. Eng. Appl. Artif. Intell. 36, 287–293 (2014)CrossRef Sangeeth, J., Jothilakshmi, S.: A novel spoken keyword spotting system using support vector machine. Eng. Appl. Artif. Intell. 36, 287–293 (2014)CrossRef
5.
Zurück zum Zitat 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)CrossRef 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)CrossRef
6.
Zurück zum Zitat 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)CrossRef 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)CrossRef
7.
Zurück zum Zitat 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) 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)
8.
Zurück zum Zitat 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) 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)
9.
Zurück zum Zitat 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)CrossRef 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)CrossRef
10.
Zurück zum Zitat 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)CrossRef 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)CrossRef
11.
Zurück zum Zitat 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) 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)
12.
Zurück zum Zitat Shriberg, E., Ferrer, L., Kajarekar, S., Venkataraman, A.: Modeling prosodic feature sequences for speaker recognition. Speech Commun. 46(3), 455–472 (2005)CrossRef Shriberg, E., Ferrer, L., Kajarekar, S., Venkataraman, A.: Modeling prosodic feature sequences for speaker recognition. Speech Commun. 46(3), 455–472 (2005)CrossRef
13.
Zurück zum Zitat 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)CrossRef 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)CrossRef
14.
Zurück zum Zitat 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)CrossRef 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)CrossRef
15.
Zurück zum Zitat 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)CrossRef 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)CrossRef
16.
Zurück zum Zitat Kinnunen, T., Li, H.: An overview of text-independent speaker recognition: from features to supervectors. Speech Commun. 52(1), 12–40 (2010)CrossRef Kinnunen, T., Li, H.: An overview of text-independent speaker recognition: from features to supervectors. Speech Commun. 52(1), 12–40 (2010)CrossRef
17.
Zurück zum Zitat 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)CrossRef 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)CrossRef
18.
Zurück zum Zitat Peng, G., Wang, W.S.-Y.: Tone recognition of continuous Cantonese speech based on support vector machines. Speech Commun. 45(1), 49–62 (2005)CrossRef Peng, G., Wang, W.S.-Y.: Tone recognition of continuous Cantonese speech based on support vector machines. Speech Commun. 45(1), 49–62 (2005)CrossRef
19.
Zurück zum Zitat 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)CrossRef 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)CrossRef
20.
Zurück zum Zitat 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)CrossRef 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)CrossRef
21.
Zurück zum Zitat Gajšek, R., Mihelič, F., Dobrišek, S.: Speaker state recognition using an HMM-based feature extraction method. Comput. Speech Lang. 27(1), 135–150 (2013)CrossRef Gajšek, R., Mihelič, F., Dobrišek, S.: Speaker state recognition using an HMM-based feature extraction method. Comput. Speech Lang. 27(1), 135–150 (2013)CrossRef
22.
Zurück zum Zitat Mohamed, A., Ramachandran Nair, K.N.: HMM/ANN hybrid model for continuous Malayalam speech recognition. Procedia Eng. 30, 616–622 (2012)CrossRef Mohamed, A., Ramachandran Nair, K.N.: HMM/ANN hybrid model for continuous Malayalam speech recognition. Procedia Eng. 30, 616–622 (2012)CrossRef
23.
Zurück zum Zitat Saeedi, N.E., Farshad, A., Farhad, T.: Support vector wavelet adaptation for pathological voice assessment. Comput. Biol. Med. 41(9), 822–828 (2011)CrossRef Saeedi, N.E., Farshad, A., Farhad, T.: Support vector wavelet adaptation for pathological voice assessment. Comput. Biol. Med. 41(9), 822–828 (2011)CrossRef
24.
Zurück zum Zitat 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)CrossRef 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)CrossRef
25.
Zurück zum Zitat 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)CrossRef 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)CrossRef
26.
Zurück zum Zitat Daqrouq, K.: Wavelet entropy and neural network for text-independent speaker identification. Eng. Appl. Artif. Intell. 24(5), 796–802 (2011)CrossRef Daqrouq, K.: Wavelet entropy and neural network for text-independent speaker identification. Eng. Appl. Artif. Intell. 24(5), 796–802 (2011)CrossRef
27.
Zurück zum Zitat Govindan, S.M., Duraisamy, P., Yuan, X.: Adaptive wavelet shrinkage for noise robust speaker recognition. Digit. Signal Proc. 33, 180–190 (2014)CrossRef Govindan, S.M., Duraisamy, P., Yuan, X.: Adaptive wavelet shrinkage for noise robust speaker recognition. Digit. Signal Proc. 33, 180–190 (2014)CrossRef
28.
Zurück zum Zitat Rossi, F., Villa, N.: Support vector machine for functional data classification. Neurocomputing 69(7-9), 730–742 (2006)CrossRef Rossi, F., Villa, N.: Support vector machine for functional data classification. Neurocomputing 69(7-9), 730–742 (2006)CrossRef
Metadaten
Titel
A Novel Approach for Speaker Recognition by Using Wavelet Analysis and Support Vector Machines
verfasst von
Kanaka Durga Returi
Vaka Murali Mohan
Y. Radhika
Copyright-Jahr
2016
Verlag
Springer India
DOI
https://doi.org/10.1007/978-81-322-2517-1_17