  We present a scoring approach for speaker verification that mimics the
standard PLDA-based backend process used in most current speaker verification
systems. However, unlike the standard backends, all parameters of the model are
jointly trained to optimize the binary cross-entropy for the speaker
verification task. We further integrate the calibration stage inside the model,
making the parameters of this stage depend on metadata vectors that represent
the conditions of the signals. We show that the proposed backend has excellent
out-of-the-box calibration performance on most of our test sets, making it an
ideal approach for cases in which the test conditions are not known and
development data is not available for training a domain-specific calibration
model.
