Skip to main content
SpringerLink
Log in

2S-Norm: A New Score Normalization for a GMM Based Text-Independent Speaker Identification System

  • Conference paper
  • First Online:
Advances in Engineering Research and Application (ICERA 2018)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 63))

Included in the following conference series:

  • 1024 Accesses

Abstract

This paper presents a new score normalization method for speaker identification using Gaussian Mixture Model (GMM). The new GMM normalization method has two main advantages: (1) the thresholds are independent to dataset and mapped to the range of \( \left[ {0\% \div 100\% } \right] \) corresponding to your expected accuracy of the system and (2) better performance comparing to common methods. The experimental results suggest the viability of the proposed approach in terms of shortening the development time and providing regular update for model’s parameters.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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. Roberts, W.J.J., Willmore, J.P.: Automatic speaker recognition using Gaussian mixture models. In: Information, Decision and Control on Proceedings, pp. 465–470. IEEE, Australia (1999)

    Google Scholar 

  2. Li, K.P., Porter, J.: Normalizations and selection of speech segments for speaker recognition scoring. In: International Conference on Acoustics, Speech, and Signal Processing on Proceedings ICASSP, pp. 595–598. IEEE, New York (1988)

    Google Scholar 

  3. Auckenthalera, R., Michael, C., Lloyd-Thomas, H.: Score normalization for text-independent speaker verification systems. Digital Sig. Process. 10, 42–54 (2000)

    Article  Google Scholar 

  4. Reynolds, D.A.: Comparison of background normalization methods for text-independent speaker verification. In: EUROSPEECH on Proceedings, Rhodes, Greece (1997)

    Google Scholar 

  5. Garcia-Romero, D., Gonzalez-Rodriguez, J., Fierrez-Aguilar, J., Ortega-Garcia, J.: U-norm likelihood normalization in pin-based speaker verification systems. In: AVBPA on Proceedings, pp. 208–213. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  6. Vandyke, D, Wagner, G.M.: R-Norm: improving inter-speaker variability modelling at the score level via regression score normalization. In: International Speech Communication Association INTERSPEECH, pp. 1–5. ISCA, New York (2013)

    Google Scholar 

  7. Scikit-learn: Machine Learning in Python. http://scikit-learn.org. Accessed 21 June 2018

  8. Luong, C.M.: Vietnam country report 2016: Updated activities on resources development for Vietnamese Speech and NLP. In: Conference of The Oriental Chapter of International Committee for Coordination and Standardization of Speech Databases and Assessment Techniques (O-COCOSDA) on Proceedings. IEEE, Bali, Indonesia (2016)

    Google Scholar 

  9. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. J. Royal Stat. Soc. Series B 39(1), 1–38 (1977)

    Google Scholar 

Download references

Acknowledgements

This work was supported by Thai Nguyen university of Technology, Vietnam.

Author information

Authors and Affiliations

  1. Thai Nguyen University of Technology, Thai Nguyen, Vietnam

    Van Huy Nguyen

Authors
  1. Van Huy Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Van Huy Nguyen .

Editor information

Editors and Affiliations

  1. Faculty of Software and Information Science, Iwate Prefectural University, Takizawa, Iwate, Japan

    Hamido Fujita

  2. Faculty of Electronic Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam

    Duy Cuong Nguyen

  3. Faculty of Mechanical Engineering, Thai Nguyen University of Technology, Thai Nguyen, Vietnam

    Ngoc Pi Vu

  4. Hanoi University of Science and technology, Hanoi, Vietnam

    Tien Long Banh

  5. Institute for Automation and Systems Engineering, Ilmenau University of Technology (TU Ilmenau), Ilmenau, Germany

    Hermann Horst Puta

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nguyen, V.H. (2019). 2S-Norm: A New Score Normalization for a GMM Based Text-Independent Speaker Identification System. In: Fujita, H., Nguyen, D., Vu, N., Banh, T., Puta, H. (eds) Advances in Engineering Research and Application. ICERA 2018. Lecture Notes in Networks and Systems, vol 63. Springer, Cham. https://doi.org/10.1007/978-3-030-04792-4_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-04792-4_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04791-7

  • Online ISBN: 978-3-030-04792-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation