Skip to main content
SpringerLink
Log in

A Robust Wavelet Based Decomposition and Multilayer Neural Network for Speaker Identification

  • Conference paper
  • First Online:
Innovations in Electronics and Communication Engineering

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 65))

Abstract

The major aim of this research paper is to recognize and figure out the problem of recognizing a speaker from its voice, we propose a new methodology for feature extraction based on speakers pitch, stationary Wavelet, and multilayered Neural Networks. In this exercise, we designed a methodology to study for Text-Dependent Speaker Identification. Wavelet analysis comprises Stationary wavelet analysis, Continuous wavelet analysis, and discrete wavelet analysis, the classification module comprises an artificial neural network, General regression forming the decision through majority test/train result scheme. A performance test is conducted using the recorded database for text dependent and text independent. Stationary wavelet with the multilayered neural network has shown better accuracy and faster identification time compared with traditional MFCC, discrete, and continuous wavelet transform approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Kumar P, Lahudkar SL (2015) International Journal on Recent and Innovation Trends in Computing and Communication 3(4):2106–2109. ISSN: 2321-81692106, IJRITCC, Apr 2015

    Google Scholar 

  2. Sarikaya R, Pellom BL, Hansen JHL (1998) Wavelet packet transform features with application to speaker identification. In: Proceedings of the IEEE Nordic Signal Processing Symposium, pp 81–84

    Google Scholar 

  3. Dan Z, Zheng S, Sun S, Dong R (2008) Speaker recognition based on LS-SVM. In: The 3rd international conference on Innovative computing Information and Control ICICIC, 2008, pp 01–04

    Google Scholar 

  4. Wang P (2005) Feature extraction bases on Mel-Scale wavelet transform for heart sound analysis. In: Proceedings of the engineering in medicine and annual conference, Shanghai, China, pp 01–05

    Google Scholar 

  5. Abdalla MI, Ali S (2010) Wavelet-based Mel-frequency cepstral coefficients for speaker identification using hidden Markov models. J Telecomm 1(2):16–21

    Google Scholar 

  6. Kekre HB, Kulkarni V (2010) Speaker identification by using vector quantization. Int J Eng Sci Technol 2(5):1325–1331

    Google Scholar 

  7. Wu J-D, Lin B-F (2009) Speaker identification using discrete wavelet packet trans-form technique with irregular position. Expert Syst Appl 36:3136–3143

    Article  Google Scholar 

  8. Suvarna Kumar G, Prasad Raju KA, Rao M et al (2010) Speaker recognition using GMM’. Int J Eng Sci Technol 2(6):2428–2436

    Google Scholar 

  9. Speaker Identification based on GFCC using GMM. Int J Innov Res Adv Eng (IJIRAE) 1(8):224. ISSN: 2349-2163. www.ijirae.com (2014), IJIRAE—All Rights Reserved

  10. Srinivasan A (2012) Speaker identification and verification using vector quantization and Mel frequency cepstral coefficients journal of applied sciences. Eng Technol 4(1):33–40. ISSN: 2040-7467 © Maxwell Scientific Organization, 2011 Published: 01 Jan 2012, pp 110–114

    Google Scholar 

  11. Kabir A, Ahsan SMM (2007) Vector quantization in text dependent automatic speaker recognition using Mel-frequency Cepstrum Coefficient. In: 6th WSEAS international conference on circuit systems, electronics control and signal processing, Cairo, Egypt, 29–31 Dec 2007, pp 352–355

    Google Scholar 

  12. Sunitha C (2015) Speaker recognition using MFCC and improved weighted vector quantization algorithm. Int J Eng Technol (IJET) 7(5):1685–1692. ISSN: 0975-4024

    Google Scholar 

  13. Dhonde SB (2015) Speaker recognition system using Gaussian mixture model. Int J Comput Appl 130(14):0975–8887, 38 Nov 2015

    Google Scholar 

  14. Pawar RV, Kajave PP, Mali SN (2005) Speaker identification using neural network. World Academy of Sci Eng Technol 12:31–35

    Google Scholar 

  15. Revada LKV, Rambatla VK, Ande KVN (2011) A novel approach to speech recognition by using generalized regression neural networks. IJCSI Int J Compute Sci Issues 1:483–489

    Google Scholar 

  16. Lu W, Sun W, Lu H (2009) Robust watermarking based on DWT and non-negative matrix factorization’. Comput Electr Eng 35:183–188

    Article  Google Scholar 

  17. Wu JD, Lin BF (2009) Speaker identification using discrete wavelet packet transform technique with irregular decomposition. Expert Syst Appl 36(2):3136–3143

    Article  Google Scholar 

  18. Vetterli M, Kovacevic J (1995) Wavelets and sub band coding. Prentice-Hall, New Jersey

    Google Scholar 

  19. Shukla A, Tiwari R, Hemant Kumar M, Kala R (2009) Speaker identification using wavelet analysis and modular neural networks. J Acoust Soc India (JASI) 36(1):14–19

    Google Scholar 

  20. Daqrouq K, Abu Hilal T, Sherif M, El-Hajjar S, Al-Qawasmi A (2009) Speaker identification system using wavelet transform and neural network. IEEE Xplore, 24 Oct 2009, pp 559–564

    Google Scholar 

  21. Hariharan M, Yaacob S, Hasrul MN, Wei OQ, Speech emotion recognition using stationary wavelet transform and timbral texture features, School of Mechatronic Engineering, University Malaysia Perlis (UniMAP)

    Google Scholar 

  22. Hariharan M, et al. (2013) Objective evaluation of speech dysfluencies using wavelet packet transform with sample entropy. Dig Sign Process A Rev J 23:952–959

    Article  MathSciNet  Google Scholar 

  23. Sindhu R, Siew-Chin Neoh, Hariharana M  (2014) A hybrid expert system approach for telemonitoring of vocal fold pathology. Int J Medical Engineering and Informatics 6(3):218–237.

    Google Scholar 

  24. Keskes H, Braham A, Lachiri Z (2013) Broken rotor bar diagnosis in induction machines through stationary wavelet packet transform and multiclass wavelet SVM. Electr Power Syst Res 97:151–157

    Article  Google Scholar 

  25. Ge Z, Iyer AN, Cheluvaraja S, Sundaram R, Ganapathiraju A (2017) Neural network based speaker classification and verification systems with enhanced features interactive intelligence. In: Intelligent systems conference, 7–8 Sept 2017, London, UK Inc., Indianapolis, Indiana

    Google Scholar 

  26. Ye J (2004) Speech recognition using time domain features from phase space reconstructions. Ph.D thesis. Marquette University Milwaukee, Wisconsin

    Google Scholar 

  27. Specht DF (1991) A general regression neural network’. IEEE Trans Neural Network 2(6):568–576

    Article  Google Scholar 

  28. Amrouche A, Rouvaen J (2006) Efficient system for speech recognition using general regression neural network. Int J Intell Technol 1(2):183–189

    Google Scholar 

  29. Almaade N, Aggoun A, Amira A (2015) Speaker identification using multimodal neural networks and wavelet analysis. IET Biom 4(1):18–28

    Article  Google Scholar 

  30. Ganchev T, Tasoulis D, Vrahatis M, Fakotakis D (2007) Generalized locally recurrent probabilistic neural networks with application to text-independent speaker verification. Neuro Comput 70:1424–1438

    Google Scholar 

  31. Wang JC, Yang CH, Wang JF, Lee HP (2007) Robust speaker identification and verification. Taiwan IEEE Comput Intell Mag 2:52–59

    Article  Google Scholar 

  32. Nijhawan G, Soni MK (2014) Speaker recognition using MFCC and vector quantisation. Int J Recent Trends Eng Technol 11(1):211–218

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, Maharashtra Institute of Technology, Aurangabad, 431005, India

    M. D. Pawar

  2. Department of Instrumentation Engineering, Government College of Engineering, Jalgaon, 425001, India

    Rajendra Kokate

Authors
  1. M. D. Pawar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajendra Kokate
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. D. Pawar .

Editor information

Editors and Affiliations

  1. Guru Nanak Institutions, Ibrahimpatnam, Telangana, India

    H. S. Saini

  2. Guru Nanak Institutions Technical Campus, Ibrahimpatnam, Telangana, India

    R. K. Singh

  3. Indian Institute of Technology Bombay, Mumbai, Maharashtra, India

    Girish Kumar

  4. Department of ECE, NIT Srinagar, Srinagar, Jammu and Kashmir, India

    G.M. Rather

  5. Department of ECE, Guru Nanak Institutions Technical Campus, Ibrahimpatnam, Telangana, India

    K. Santhi

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Pawar, M.D., Kokate, R. (2019). A Robust Wavelet Based Decomposition and Multilayer Neural Network for Speaker Identification. In: Saini, H., Singh, R., Kumar, G., Rather, G., Santhi, K. (eds) Innovations in Electronics and Communication Engineering. Lecture Notes in Networks and Systems, vol 65. Springer, Singapore. https://doi.org/10.1007/978-981-13-3765-9_21

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-3765-9_21

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-3764-2

  • Online ISBN: 978-981-13-3765-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation