  Decision making is an important component in a speaker verification system.
For the conventional GMM-UBM architecture, the decision is usually conducted
based on the log likelihood ratio of the test utterance against the GMM of the
claimed speaker and the UBM. This single-score decision is simple but tends to
be sensitive to the complex variations in speech signals (e.g. text content,
channel, speaking style, etc.). In this paper, we propose a decision making
approach based on multiple scores derived from a set of cohort GMMs (cohort
scores). Importantly, these cohort scores are not simply averaged as in
conventional cohort methods; instead, we employ a powerful discriminative model
as the decision maker. Experimental results show that the proposed method
delivers substantial performance improvement over the baseline system,
especially when a deep neural network (DNN) is used as the decision maker, and
the DNN input involves some statistical features derived from the cohort
scores.
