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Proceedings Paper

PCA/LDA approach for text-independent speaker recognition
Author(s): Zhenhao Ge; Sudhendu R. Sharma; Mark J. T. Smith
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Paper Abstract

Various algorithms for text-independent speaker recognition have been developed through the decades, aiming to improve both accuracy and efficiency. This paper presents a novel PCA/LDA-based approach that is faster than traditional statistical model-based methods and achieves competitive results. First, the performance based on only PCA and only LDA is measured; then a mixed model, taking advantages of both methods, is introduced. A subset of the TIMIT corpus composed of 200 male speakers, is used for enrollment, validation and testing. The best results achieve 100%, 96% and 95% classification rate at population level 50, 100 and 200, using 39- dimensional MFCC features with delta and double delta. These results are based on 12-second text-independent speech for training and 4-second data for test. These are comparable to the conventional MFCC-GMM methods, but require significantly less time to train and operate.

Paper Details

Date Published: 10 May 2012
PDF: 11 pages
Proc. SPIE 8401, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X, 840108 (10 May 2012); doi: 10.1117/12.919235
Show Author Affiliations
Zhenhao Ge, Purdue Univ. (United States)
Sudhendu R. Sharma, Purdue Univ. (United States)
Mark J. T. Smith, Purdue Univ. (United States)


Published in SPIE Proceedings Vol. 8401:
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X
Harold Szu, Editor(s)

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