  Unsupervised domain adaptation of speech signal aims at adapting a
well-trained source-domain acoustic model to the unlabeled data from target
domain. This can be achieved by adversarial training of deep neural network
(DNN) acoustic models to learn an intermediate deep representation that is both
senone-discriminative and domain-invariant. Specifically, the DNN is trained to
jointly optimize the primary task of senone classification and the secondary
task of domain classification with adversarial objective functions. In this
work, instead of only focusing on learning a domain-invariant feature (i.e. the
shared component between domains), we also characterize the difference between
the source and target domain distributions by explicitly modeling the private
component of each domain through a private component extractor DNN. The private
component is trained to be orthogonal with the shared component and thus
implicitly increases the degree of domain-invariance of the shared component. A
reconstructor DNN is used to reconstruct the original speech feature from the
private and shared components as a regularization. This domain separation
framework is applied to the unsupervised environment adaptation task and
achieved 11.08% relative WER reduction from the gradient reversal layer
training, a representative adversarial training method, for automatic speech
recognition on CHiME-3 dataset.

Computation and Language;Artificial Intelligence;Sound;Audio and Speech Processing