Score Normalization

The score normalization techniques aim, generally, to reduce the scores variabilities in order to facilitate the estimation of a unique speaker-independent threshold during the decision step. Most of the current normalization techniques are based on the estimation of the impostors scores distribution where the mean, μ, and the standard deviation v, depend on the considered speaker model and/or test utterance. These mean and standard deviation values will then be used to normalize any incoming score s using the normalization function

$${\rm{score}}N(s){{s - \mu } \over v}.$$

Two main score normalization techniques used in speaker recognition are:

• 1. Znorm. The zero normalization (Znorm) method (and its variants like Hnorm (Heck, L.P., Weintraub, M.: Handset-dependent background models for robust text-independent speaker recognition. In: ICASSP. (1997))) normalizes the score distribution using the claimed speaker statistics. In other words, the claimed speaker model is tested against a...