DocumentCode :
2821696
Title :
Dimensionality reduction for text-independent speaker identification using Gaussian mixture model
Author :
El-Gamal, M.A. ; Abu El-Yazeed, M.F. ; El Ayadi, M.M.H.
Author_Institution :
Dept. of Eng. Phys. & Math., Cairo Univ., Giza
Volume :
2
fYear :
2003
fDate :
30-30 Dec. 2003
Firstpage :
625
Abstract :
Reducing the dimensionality of the training and testing data is crucial for text-independent speaker identification tasks. In this paper, the performance of various dimensionality reduction techniques is evaluated for speaker identification systems using Gaussian mixture model (GMM) as the statistical classifier. An enhancement of the standard linear discriminant analysis (LDA) is proposed in which class distributions are assumed to follow Gaussian mixture distribution. This assumption is more appropriate for asymmetric and multimodal class conditional densities. In addition, a new feature selection technique based on the QR factorization method is introduced. Computer simulation results reveal that the proposed modification to the LDA outperforms the standard algorithm in terms of classification accuracy. Moreover, the QR-based selection technique produces comparable results to other prominent dimensionality reduction techniques
Keywords :
Gaussian distribution; feature extraction; speaker recognition; Gaussian mixture model; QR factorization method; computer simulation; dimensionality reduction; feature selection; linear discriminant analysis; multimodal class conditional density; statistical classifier; text-independent speaker identification; Computer simulation; Covariance matrix; Feature extraction; Linear discriminant analysis; Loudspeakers; Physics; Principal component analysis; Speech; Testing; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Circuits and Systems, 2003 IEEE 46th Midwest Symposium on
Conference_Location :
Cairo
ISSN :
1548-3746
Print_ISBN :
0-7803-8294-3
Type :
conf
DOI :
10.1109/MWSCAS.2003.1562364
Filename :
1562364
Link To Document :
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