  The teacher-student (T/S) learning has been shown effective in unsupervised
domain adaptation [1]. It is a form of transfer learning, not in terms of the
transfer of recognition decisions, but the knowledge of posteriori
probabilities in the source domain as evaluated by the teacher model. It learns
to handle the speaker and environment variability inherent in and restricted to
the speech signal in the target domain without proactively addressing the
robustness to other likely conditions. Performance degradation may thus ensue.
In this work, we advance T/S learning by proposing adversarial T/S learning to
explicitly achieve condition-robust unsupervised domain adaptation. In this
method, a student acoustic model and a condition classifier are jointly
optimized to minimize the Kullback-Leibler divergence between the output
distributions of the teacher and student models, and simultaneously, to
min-maximize the condition classification loss. A condition-invariant deep
feature is learned in the adapted student model through this procedure. We
further propose multi-factorial adversarial T/S learning which suppresses
condition variabilities caused by multiple factors simultaneously. Evaluated
with the noisy CHiME-3 test set, the proposed methods achieve relative word
error rate improvements of 44.60% and 5.38%, respectively, over a clean source
model and a strong T/S learning baseline model.
