Voice Based Authentication System for Web Applications using Machine Learning

##plugins.themes.academic_pro.article.sidebar##

Published Nov 26, 2022
https://doi.org/10.47164/ijngc.v13i5.966
Download
Requires Subscription PDF
Statistic

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...
Volume 13, Special Issue 5, November 2022

##plugins.themes.academic_pro.article.main##

Rakesh K Kadu
Purshottam J Assudani
Sahil Bhojane
Tanish Agrawal
Vidhi Siddhawar
Yash Kale

Abstract

Due to security concerns, the biometric trend is being used in many systems. Biometric authentication is a cheap, easy, and reliable technology for multi-factor authentication. Cryptosystems are one such example of using biometric data. However, this could be risky as biometric information is saved for authentication purposes. Therefore, voice biometric systems provide more efficient security and unique identity than commonly used biometric systems. Although, speech recognition-based authentication systems suffer from replay attacks. In this paper, we implement and analyze a text-independent voice-based biometric authentication system based on the randomly generated input text. Since the prompted text phrase is not known to the speaker in advance, it is difficult to launch replay attacks. The system uses Mel-Frequency Cepstrum Coefficients (MFCC) to extract speech features and Gaussian Mixture Models (GMM) for speaker modeling.

##plugins.themes.academic_pro.article.details##

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Rakesh K Kadu, Purshottam J Assudani, Sahil Bhojane, Tanish Agrawal, Vidhi Siddhawar, & Yash Kale. (2022). Voice Based Authentication System for Web Applications using Machine Learning. International Journal of Next-Generation Computing, 13(5). https://doi.org/10.47164/ijngc.v13i5.966
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. A. Maesa, F. Garzia, M. S. and Cusani, R. 2012. Text independent automatic speaker
  2. recognition system using mel-frequency cepstrum coefficient and gaussian mixture models.
  3. Journal of Information Security Vol.3, pp.335–340.
  4. Han, W., Chan, C.-F., Choy, C.-S., and Pun, K.-P. 2006. An efficient mfcc extraction
  5. method in speech recognition. In 2006 IEEE International Symposium on Circuits and
  6. Systems (ISCAS). IEEE, 4–pp.
  7. Khan, S. P. A. H. 2022. Google scholar citation: Ijaeml. International Journal of Applied Engineering
  8. and Management Letters (IJAEML) A Refereed International Journal of Srinivas
  9. University Vol.6, pp.2581–7000.
  10. Maty´aˇs, V. and ˇR´ıha, Z. 2010. Security of biometric authentication systems. 2010 International DOI: https://doi.org/10.1109/CISIM.2010.5643698
  11. Conference on Computer Information Systems and Industrial Management Applications,
  12. CISIM 2010 , pp.19–28.
  13. Nassif, A. B., Shahin, I., Attili, I., Azzeh, M., and Shaalan, K. 2019. Speech recognition
  14. using deep neural networks: A systematic review. IEEE Access 7, 19143–19165. DOI: https://doi.org/10.1109/ACCESS.2019.2896880
  15. Reddy, D. R. 1976. Speech recognition by machine: A review. Proceedings of the IEEE 64, 4, DOI: https://doi.org/10.1109/PROC.1976.10158
  16. –531.
  17. Reynolds, D. A. and Rose, R. C. 1995. Robust text-independent speaker identification
  18. using gaussian mixture speaker models. IEEE Transactions on Speech and Audio Processing
  19. Vol.3, pp.72–83.
  20. Sahidullah, M. and Saha, G. 2012. Design, analysis and experimental evaluation of block
  21. based transformation in mfcc computation for speaker recognition. IEEE Access Vol.54,
  22. pp.543–565.
  23. Tong, S., Chen, N., Qian, Y., and Yu, K. 2014. Evaluating vad for automatic speech DOI: https://doi.org/10.1109/ICOSP.2014.7015406
  24. recognition. In 2014 12th International Conference on Signal Processing (ICSP). IEEE,
  25. –2314.

Most read articles by the same author(s)