On the Use of Voting Methods for Speaker Identification Based on Various Resolution Filterbanks

This paper proposes a novel speaker identification system based on score fusion of various resolution filterbanks. The proposed system uses multiple features which are extracted from filterbanks having various spectral resolutions. Each speaker model is constructed by independent feature set, but the system makes final decision by combining the outcome of each model. We introduce several well-known voting methods for decision. Simulation results using TIMIT database show that the proposed score fusion method significantly improves speaker identification performance compared to single model one. Especially, 59.28% of relative improvement is achieved by using a product rule.