How to deal with multiple-targets in speaker identification systems?

In open-set speaker identification systems a known phenomenon is that the false alarm (accept) error rate increases dramatically when increasing the number of registered speakers (models). In this paper, we demonstrate this phenomenon and suggest a solution using a new model-dependent score-normalization technique, called Top-norm. The Top-norm method was specifically developed to improve results of open-set speaker identification systems. Also, we suggest a score-normalization parameter adaptation technique. Experiments performed using speaker recognition corpora arc described and demonstrate that the new method outperforms other normalization methods.