  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.
