PCA/LDA Approach for Text-Independent Speaker Recognition
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.
Cite
@article{arxiv.1602.08045,
title = {PCA/LDA Approach for Text-Independent Speaker Recognition},
author = {Zhenhao Ge and Sudhendu R. Sharma and Mark J. T. Smith},
journal= {arXiv preprint arXiv:1602.08045},
year = {2016}
}
Comments
Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series