Robust Text-Independent Speaker Identification Using Hybrid PCA&LDA

We have been building a text-independent speaker recognition system in noisy conditions. In this paper, we propose a novel feature using hybrid PCA/LDA. The feature is created from the convectional MFCC(mel-frequency cepstral coefficients) by transforming them using a matrix. The matrix consists of some components from the PCA and LDA transformation matrices. We tested the new feature using Aurora project Database 2 which is intended for the evaluation of algorithms for front-end feature extraction algorithms in background noise. The proposed method outperformed in all noise types and noise levels. It reduced the relative recognition error by 63.6% than using the baseline feature when the SNR is 15dB.