Robust speaker identification system using multi-band dominant features with empirical mode decomposition

Abstract

This paper presents a text independent speaker identification system using multi-band features with artificial neural network. Linear predictive cepstrum coefficients (LPCCs) computed from sub-band signals with higher order statistics (HOS) are employed as the main features to represent the speaker characteristics. The multi-band representation of the speech signal is implemented by empirical mode decomposition (EMD). Dominant feature vectors are derived by applying principal component analysis (PCA) on LPCC space computed from the speech signal. The experimental results show that the proposed system improves the speaker identification performance. The efficiency is also compared for different features with noisy speech signals.