Advertisement

International Journal of Speech Technology

, Volume 21, Issue 1, pp 157–165 | Cite as

A novel whispered speaker identification system based on extreme learning machine

  • J. Sangeetha
  • T. Jayasankar
Article
  • 89 Downloads

Abstract

Whispered speech speaker identification system is one of the most demanding efforts in automatic speaker recognition applications. Due to the profound variations between neutral and whispered speech in acoustic characteristics, the performance of conventional speaker identification systems applied on neutral speech degrades drastically when compared to whisper speech. This work presents a novel speaker identification system using whispered speech based on an innovative learning algorithm which is named as extreme learning machine (ELM). The features used in this proposed system are Instantaneous frequency with probability density models. Parametric and nonparametric probability density estimation with ELM was compared with the hybrid parametric and nonparametric probability density estimation with Extreme Learning Machine (HPNP-ELM) for instantaneous frequency modeling. The experimental result shows the significant performance improvement of the proposed whisper speech speaker identification system.

Keywords

Speaker identification Whispered speech identification MFCC Extreme learning machine 

References

  1. Campbell, W. M., Campbell, J. P., Reynolds, D. A., Singer, E., & Torres-Carrasquillo, P. A. (2006). Support vector machines for speaker and language recognition. Computer Speech & Language, 20(2), 210–229.CrossRefGoogle Scholar
  2. Campbell, W. M., Sturim, D. E., & Reynolds, D. A. (2006). Support vector machines using GMM supervectors for speaker verification. IEEE Signal Processing Letters, 13(5), 308–311.CrossRefGoogle Scholar
  3. Fan, X., & Hansen, J. H. (2011). Speaker identification within whispered speech audio streams. IEEE transactions on Audio, Speech, and Language Processing, 19(5), 1408–1421.CrossRefGoogle Scholar
  4. Gu, X., & Zhao, H. (2010). Whispered speech speaker identification based on SVM and FA. In Audio Language and Image Processing (ICALIP), 2010 International Conference on (pp. 757–760). IEEE.Google Scholar
  5. Haim, P., Joseph, F., & Ian, J. (2006). A study of Gaussian mixture models ofcolor and texture features for image classification and segmentation. Pattern Recognition, 39(4), 695–706, 2006.Google Scholar
  6. Huang, G. B., Wang, D., Lan, Y. (2011). Extreme learning machine: A survey. International Journal of Machine Learning and Cybernetics, 2, 107–122.CrossRefGoogle Scholar
  7. Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70(1), 489–501.CrossRefGoogle Scholar
  8. Ito, T., Takeda, K., & Itakura, F. (2005). Analysis and recognition of whispered speech. Speech Communication, 45(2), 139–152.CrossRefGoogle Scholar
  9. Jain, K., Ross, A., & Prabhakar, S. (2004). An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology, 14(1), 4–20.CrossRefGoogle Scholar
  10. Jin, Q., Jou, S.-C. S., & Schultz, T. (2007). Whispering speaker identification. In Multimedia and Expo, 2007 IEEE International Conference on, pp. 1027–1030.Google Scholar
  11. John, H. L. (2007). Analysis and classification of speech Mode: Whispered through shouted. 8th Annual Conference of the International Speech Communication Association, Interspeech.Google Scholar
  12. Jovičić, S. T. (1998). Formant feature differences between whispered and voiced sustained vowels. Acta Acustica United with Acustica, 84(4), 739–743.Google Scholar
  13. Jovičić, S. T., & Šarić, Z. (2008). Acoustic analysis of consonants in whispered speech. Journal of Voice, 22(3), 263–274.CrossRefGoogle Scholar
  14. Li, Q. (2001). A detection approach to search-space reduction for HMM state alignment in speaker verification. IEEE Transactions on Speech and Audio Processing, 9(5), 569–578.CrossRefGoogle Scholar
  15. Mak, M. W., & Kung, S. Y. (2000). Estimation of elliptical basis function parameters by the EM algorithm with application to speaker verification. IEEE Transactions on Neural Networks, 11(4), 961–969.CrossRefGoogle Scholar
  16. Morris, R. W., & Clements, M. A. (2002). Reconstruction of speech from whispers. Medical Engineering & Physics, 24(7), 515–520.CrossRefGoogle Scholar
  17. Oyang, Y. J., Ou, Y. Y., Hwang, S. C., Chenl, C. Y., & Chang, D. T. H. (2005). Data classification with a relaxed model of variable kernel density estimation. In Proc. IEEE Int. Joint Conf. Neural Netw, vol. 5, pp. 2831–2836.Google Scholar
  18. Pellom, L., & Hansen, J. H. L. (1998). An efficient scoring algorithm for Gaussian mixture model based speaker identification. IEEE Signal Processing Letters, 5(11) 281–284.CrossRefGoogle Scholar
  19. Poignant, J., Besacier, L., & Quenot, G. (2014). Unsupervised speaker identification in TV broadcast based on written names. IEEE/ACM Transactions on Audio, Speech and Language Processing, 23, 57–68.Google Scholar
  20. Sadjadi, S. O., & Hansen, J. H. L. (2014). Blind spectral weighting for robust speaker identification under reverberation mismatch. IEEE/ACM Transactions on Audio, Speech and Language Processing, 22(5), 937–945.CrossRefGoogle Scholar
  21. Wang, J. C., Chin, Y. H., Hsieh, W. C., Lin, C. H., Chen, Y. R., & Siahaan, E. (2015). Speaker identification with whispered speech for the access control system. IEEE Transactions on Automation Science and Engineering, 12(4), 1191–1199.CrossRefGoogle Scholar
  22. Wang, J. C., Yang, C. H., Wang, J. F., & Lee, H. P. (2007). Robust speaker identification and verification.” IEEE Computational Intelligence Magazine, 2(2), 52–59.CrossRefGoogle Scholar
  23. Xu, J., & Zhao, H. (2012). Speaker identification with whispered speech using unvoiced-consonant phonemes. In Proc. Int. Conf. Image Anal. Signal Process, pp. 9–11.Google Scholar
  24. Zhang, C., & Hansen, J. H. (2007). Analysis and classification of speech mode: Whispered through shouted. In Interspeech (Vol. 7, pp. 2289–2292).Google Scholar
  25. Zhao, Y., Wang, & Wang, D. (2014). Robust speaker identification in noisy and reverberant conditions. IEEE/ACM Transactions on Audio, Speech and Language Processing, 22(4), 836–845.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of IT/SOCSASTRA Deemed UniversityThanjavurIndia
  2. 2.Department of ECEAnna UniversityTrichirappalliIndia

Personalised recommendations