The week's pick
Reseach Article
Dimensionality Reduction in Feature Vector using Principle Component Analysis (PCA) for Effective Speaker Recognition
| International Journal of Applied Information Systems |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 5 - Number 5 |
| Year of Publication: 2013 |
| Authors: Suri Babu Korada, Anitha. Y, Anjana. K. K. V. S |
10.5120/ijais13-450913
|
Suri Babu Korada, Anitha. Y, Anjana. K. K. V. S . Dimensionality Reduction in Feature Vector using Principle Component Analysis (PCA) for Effective Speaker Recognition. International Journal of Applied Information Systems. 5, 5 ( April 2013), 15-17. DOI=10.5120/ijais13-450913
Abstract
This paper describes analysis of a speaker recognition model based on Generalized Gamma Distribution (GGD) using PCA. The proposed work mainly concentrates on the feature vectors that are generated from the speech signals contain high dimension data, but to model a speech and recognize a speaker finite speech samples which plays significant role in speech analysis are sufficient, hence it necessary to reduce dimension of the data. The PCA is considered for this purpose, it converts high dimension speech signal in to a low dimension speech signal by transforming the un-correlated components of the speech signal. PCA not only reduces the correlation among feature vectors but also the speech signal. The feature vectors are modeled by extracting MFCC followed by PCA for dimensionality reduction.
References
- K. Suri Babu et al "Speaker recognition model based on generalized gamma distribution using compound transformed dynamic feature vector", communicated to Asian journal of science and technology"International Journal of Embedded Systems and Applications (IJESA) Vol. 2, No. 3, September 2012.
- Lawrence R. Rabiner,(1989), A Tutorial on HMM & Selected Applications in speech Recognition, proceedings of IEEE vol-77,No-2,feb-1989,pp257-284.
- Md. RashidulHasan, et al(2004),Speaker identificationusing Mel Frequency Cepstral Coefficients,3rd International Conference on Electrical & Computer Engineering,ICECE 2004, 28-30 December 2004, Dhaka, Bangladesh.
- Suribabukorada et al(2011), "Text Independent Speaker Recognition Model Based On Gamma Distribution Using Delta, Shifted Delta Cepstrals" published in Springer link conference (SPPR-2012).
- CorneliuOctavian. D,I. Gavat,(2005),Feature Extraction Modeling &Training Strategies in continuous speech Recognition For Roman Language, EU Proceedings of IEEE Xplore,EUROCN-2005,pp-1424-1428.
- suribabukorada et al,(2011), "Text Dependent and Gender Independent Speaker Recognition Model based on Generalizations of Gamma Distribution" International Journal of Computer Applications (0975 – 8887) Volume 35– No. 6, December 2011
- Eddie Wong and SridhaSridharan ,(2001),Comparison of Linear Prediction Cepstrum Coefficients and Mel-Frequency Cepstrum Coefficients for Language Identification,lnternational Symposium on Intelligent Multimedia, Video and Speech Processing. May 24 2001 Hong Kong.
- Douglas. A. Reynolds,member,IEEE and Richard. C. Rose,member,IEEE, Robust text-Independent Speaker Identification Using Gaussian Mixture Speaker Models,IEEE Transactions on speech and audio processing,vol. 3No. 1,january1995.
- George Almpanidis and Constantine Kotropoulos,(2006)voice activity detection with generalized gamma distribution, IEEE,ICME 2006.
- Marko kos, DamjanVlaj,ZdravkoKacic,(2011)"Speaker's gender classification and segmentation using spectral and cepstral feature averaging", 18th International Conference on Systems, Signals and Image Processing - IWSSIP 2011 .
- DayanaRibasGonzalez,JoseR. Calvo de Lara(2009),"Speaker verification with shifted delta cepstral features:Its Pseudo-Prosodic Behaviour"proc I Iberian SLTech 2009.
Index Terms
Keywords