  High accuracy speech recognition requires a large amount of transcribed data
for supervised training. In the absence of such data, domain adaptation of a
well-trained acoustic model can be performed, but even here, high accuracy
usually requires significant labeled data from the target domain. In this work,
we propose an approach to domain adaptation that does not require
transcriptions but instead uses a corpus of unlabeled parallel data, consisting
of pairs of samples from the source domain of the well-trained model and the
desired target domain. To perform adaptation, we employ teacher/student (T/S)
learning, in which the posterior probabilities generated by the source-domain
model can be used in lieu of labels to train the target-domain model. We
evaluate the proposed approach in two scenarios, adapting a clean acoustic
model to noisy speech and adapting an adults speech acoustic model to children
speech. Significant improvements in accuracy are obtained, with reductions in
word error rate of up to 44% over the original source model without the need
for transcribed data in the target domain. Moreover, we show that increasing
the amount of unlabeled data results in additional model robustness, which is
particularly beneficial when using simulated training data in the
target-domain.
