  In a recent work, we presented a discriminative backend for speaker
verification that achieved good out-of-the-box calibration performance on most
tested conditions containing varying levels of mismatch to the training
conditions. This backend mimics the standard PLDA-based backend process used in
most current speaker verification systems, including the calibration stage. All
parameters of the backend are jointly trained to optimize the binary
cross-entropy for the speaker verification task. Calibration robustness is
achieved by making the parameters of the calibration stage a function of
vectors representing the conditions of the signal, which are extracted using a
model trained to predict condition labels. In this work, we propose a
simplified version of this backend where the vectors used to compute the
calibration parameters are estimated within the backend, without the need for a
condition prediction model. We show that this simplified method provides
similar performance to the previously proposed method while being simpler to
implement, and having less requirements on the training data. Further, we
provide an analysis of different aspects of the method including the effect of
initialization, the nature of the vectors used to compute the calibration
parameters, and the effect that the random seed and the number of training
epochs has on performance. We also compare the proposed method with the
trial-based calibration (TBC) method that, to our knowledge, was the
state-of-the-art for achieving good calibration across varying conditions. We
show that the proposed method outperforms TBC while also being several orders
of magnitude faster to run, comparable to the standard PLDA baseline.
