Speaker identification using pairwise log-likelihood ratio measures

T-norm and GMM-UBM are two predominant log-likelihood ratio (LLR)-based approaches for speaker verification in the last decade. In this paper, we embed T-norm and GMM-UBM in the new approaches based on cross likelihood ratio (CLR), named the pairwise LLR measures, for speaker identification tasks. This pairwise LLR measures can provide some extent of compensation for the conventional speaker identification method especially when client speaker models are not robust. Our experimental results show that the proposed pairwise LLR methods outperform the conventional GMM-UBM speaker identification approach.