VQ score normalisation for text-dependent and text-independent speaker recognition

Abstract

Individual weighting of speaker models in VQ-based recognition has some advantages but means that scores from different models may not be directly comparable, so making identification difficult. It is also problematic for verification as decision thresholds cannot easily be set without first testing models with genuine and imposter utterances. We present a novel normalisation method for VQ speaker recognition which applies an offset to each model, based on the average score between it and the imposter models, to bring particularly high- or low-scoring models into line with the general score range. It may be calculated a priori, before running any actual tests. The method works for both text-dependent and text-independent tasks and improves both the identification and verification error rates.