Improved normalization without recourse to an impostor database for speaker verification

Score normalization has become an important facet of modern speaker verification systems. That is, the score of the verification attempt with the claimant voiceprint is usually normalized by the score from a background model or a cohort model. Typically, the determination of these normalizing models requires a priori a database of impostors and is clearly incompatible with real world applications. We propose a scheme to generate a normalizing model by biasing a speaker independent model with the customer´s enrollment tokens. For each password of each customer, there exists a privileged direction in model parameter space defined by the speaker independent and dependent models. By changing the location of the speaker independent model along this direction, a family of modified normalizing models can be generated without any knowledge about the impostors. In the difficult task of speaker verification with same-gender impostors, who know the password, 10%-15% error rate reduction is achieved using various biasing mechanisms