Score weighting in speaker verification systems

Abstract

This paper presents a method for re-weighting the frame-based scores of a speaker recognition system according to the discrimination level of the best matched Gaussian mixture for that frame. This approach focuses on particular feature space regions that either have been modeled accurately or contain the phonemes which are inherently most discriminative. The performance of individual Gaussian mixtures in terms of Equal Error Rate (EER) and minimum Detection Cost Function (DCF) on training, development and testing datasets consistently suggest that some Gaussian mixtures are inherently more discriminative regardless of their occurrence in training data. Therefore, it is possible to enhance the performance of speaker verification systems by re-weighting the frames that are mainly produced by those discriminative Gaussian mixtures. Compared with the baseline, results show a relative improvement of 5.82% and 5.46% on male speakers from the NIST 2002 dataset, in terms of EER and min DCF, respectively.

Robust text-independent speaker verification in environmental noise

“…The temporally weighted features [54] and score weighting described in [55] are examples of this class of techniques.…”