The week's pick
Random Articles
Reseach Article
Speaker Recognition System using Gaussian Mixture Model
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 130 - Number 14 |
| Year of Publication: 2015 |
| Authors: Athira Aroon, S.B. Dhonde |
10.5120/ijca2015907193
|
Athira Aroon, S.B. Dhonde . Speaker Recognition System using Gaussian Mixture Model. International Journal of Computer Applications. 130, 14 ( November 2015), 38-40. DOI=10.5120/ijca2015907193
Abstract
In this paper,features for text-independent speaker recognition has been evaluated. Speaker identification from a set of templates and analyzing speaker recognition rate by extracting several key features like Mel Frequency Cepstral Coefficients [MFCC] from the speech signals of those persons by using the process of feature extraction using MATLAB2013 .These features are effectively captured using feature matching technique like Gaussian Mixture Model [GMM] , with varying mixture components of mixture model and the analyzing its effect on recognition rate . Improve the speaker recognition rate by varying the input parameters of the classifier. The experiments are evaluated on TIMIT Database effectively for a speech signal sampled at 16kHz.
References
- Campbell J.P. and Jr. “Speaker recognition: A Tutorial” Proceeding of the IEEE. Vol 85, 1437- 1462 1997.
- S.Furui. “Fifty years of progress in speech and speaker recognition,” Proceedings ASA Meeting, 2004.
- Tomi Kinnunen., and Haizhou Li., An overview of Text-Independent Speaker Recognition: from Features to Supervectors. Speech Communication, July 1, 2009.
- Kinnunen, T., Hautamaki, V., and Franti, P. On the use of long-term average spectrum in automatic speaker recognition. In 5th Int. Symposium on Chinese Spoken Language Processing (December 2006), pp.559-567.
- Yuan Yujin, Zhao Peihua, Zhou Qun,, “Research of speaker recognition based on combination of LPCC and MFCC”, IEEE International Conference , Oct. 2010, pp.765-767.
- D. A. Reynolds, A Gaussian mixture modeling approach to text independent speaker identification, Ph.D. thesis, Georgia Institute of Technology, Atlanta, Ga, USA, September 1992.
- D. A. Reynolds, “Speaker identification and verification using Gaussian mixture speaker models,” Speech Communication, vol. 17, no. 1-2, pp. 91–108, 1995.”
- Reynolds, D. "Speaker Verification Using Adapted Gaussian Mixture Models." Digital Signal Processing 10.13 (2000): 19‐41. Print.
- Lu, X. and J. Dang (2008). An investigation of dependencies between frequency components and speaker characteristics for text-independent speaker identification. Speech Communication, 50(4), 312–322.
- Sinith, M.S., Salim, A., Gowri Sankar, K., Sandeep Narayanan, K.V. Soman, V., “A novel method for Text-Independent speaker identification using MFCC and GMM”, , 2010 International Conference, Nov. 2010, pp.292-296
- Adami.A, Mihaescu.R, Reynolds.D, and Godfrey.J., Modelling Prosodic dynamics for speaker recognition. In Proc. Int. Conf. on Acoustics, Speech, and Signal Processing , April 2003), pp. 788-791.
Index Terms
Keywords