This book develops transfer learning paradigms for spoken language processing applications.In
particular we tackle domain adaptation in the context of Automatic Speech Recognition (ASR)
and Cross-Lingual Learning in Automatic Speech Translation (AST). The first part of the book
develops an algorithm for unsupervised domain adaptation of End-to-End ASR models. In recent
years ASR performance has improved dramatically owing to the availability of large annotated
corpora and novel neural network architectures. However the ASR performance drops considerably
when the training data distribution does not match the distribution that the model encounters
during deployment (target domain). A straightforward remedy is collecting labeled data in the
target domain and re-training the source domain ASR model. However it is often expensive to
collect labeled examples while unlabeled data is more accessible. Hence there is a need for
unsupervised domain adaptation methods. To that end we develop a simple but effective
adaptation algorithm called the Dropout Uncertainty-Driven Self-Training (DUST). DUST
repurposes the classic Self-Training (ST) algorithm to make it suitable for the domain
adaptation problem.
