  Pseudo-label (PL) filtering forms a crucial part of Self-Training (ST)
methods for unsupervised domain adaptation. Dropout-based Uncertainty-driven
Self-Training (DUST) proceeds by first training a teacher model on source
domain labeled data. Then, the teacher model is used to provide PLs for the
unlabeled target domain data. Finally, we train a student on augmented labeled
and pseudo-labeled data. The process is iterative, where the student becomes
the teacher for the next DUST iteration. A crucial step that precedes the
student model training in each DUST iteration is filtering out noisy PLs that
could lead the student model astray. In DUST, we proposed a simple, effective,
and theoretically sound PL filtering strategy based on the teacher model's
uncertainty about its predictions on unlabeled speech utterances. We estimate
the model's uncertainty by computing disagreement amongst multiple samples
drawn from the teacher model during inference by injecting noise via dropout.
In this work, we show that DUST's PL filtering, as initially used, may fail
under severe source and target domain mismatch. We suggest several approaches
to eliminate or alleviate this issue. Further, we bring insights from the
research in neural network model calibration to DUST and show that a
well-calibrated model correlates strongly with a positive outcome of the DUST
PL filtering step.
