  The performance of automatic speech recognition (ASR) systems typically
degrades significantly when the training and test data domains are mismatched.
In this paper, we show that self-training (ST) combined with an
uncertainty-based pseudo-label filtering approach can be effectively used for
domain adaptation. We propose DUST, a dropout-based uncertainty-driven
self-training technique which uses agreement between multiple predictions of an
ASR system obtained for different dropout settings to measure the model's
uncertainty about its prediction. DUST excludes pseudo-labeled data with high
uncertainties from the training, which leads to substantially improved ASR
results compared to ST without filtering, and accelerates the training time due
to a reduced training data set. Domain adaptation experiments using WSJ as a
source domain and TED-LIUM 3 as well as SWITCHBOARD as the target domains show
that up to 80% of the performance of a system trained on ground-truth data can
be recovered.
