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10 May 2012 PCA/LDA approach for text-independent speaker recognition
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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.
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Zhenhao Ge, Sudhendu R. Sharma, and Mark J. T. Smith "PCA/LDA approach for text-independent speaker recognition", Proc. SPIE 8401, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X, 840108 (10 May 2012); https://doi.org/10.1117/12.919235
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