Effective use of combined excitation source and vocal-tract information for speaker recognition tasks

Abstract

In automatic speaker recognition (SR) tasks the widely used score level combination scheme derives a general consensus from the independent opinions of individual evidences. Instead, we conjecture that collectively contributed decisions may be more effective. Based on this idea this work proposes an effective combination scheme, where the vocal-tract and excitation source information take decisions collectively, resulting higher improvements in SR accuracy. In the proposed scheme, independently made feature-specific models are padded for building resultant models. While testing, feature-specific test features are padded in similar fashion, and then used for comparison with resultant models. The main advantage of this proposed scheme is that it does not require any ground truth information for combined use of multiple evidences like in score level combination scheme. The potential of the proposed scheme is experimentally demonstrated by conducting different speaker recognition experiments in clean and noisy conditions, and also comparative studies with score level fusion scheme as reference. The TIMIT database is used for studies with clean case, and Indian Institute of Technology Guwahati Multi-Variability (IITG-MV) databases for noisy case. In clean case the proposed scheme provides relatively 1% of higher improvements in performance for GMM based speaker identification system and 8.5% for GMM–UBM based speaker verification system. In noisy case the corresponding parameters are 1% and 3%, respectively. The final evaluations on NIST-2003 database with GMM–UBM and i-vector based systems show relatively higher improvements in performance by 5.17% and 4.73%, respectively. The proposed scheme is observed to be statistically more significant than the commonly used score level fusion of multiple evidences.