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arXiv · 1602.08045

PCA/LDA Approach for Text-Independent Speaker Recognition

Zhenhao Ge·Sudhendu R. Sharma·Mark J. T. Smith
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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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BibTeXRIS

Zhenhao Ge, Sudhendu R. Sharma, Mark J. T. Smith. 2016-02-25. PCA/LDA Approach for Text-Independent Speaker Recognition. https://doi.org/10.1117/12.919235

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