  This thesis focuses on representation learning for sequence data over time or
space, aiming to improve downstream sequence prediction tasks by using the
learned representations. Supervised learning has been the most dominant
approach for training deep neural networks for learning good sequential
representations. However, one limiting factor to scale supervised learning is
the lack of enough annotated data. Motivated by this challenge, it is natural
to explore representation learning methods that can utilize large amounts of
unlabeled and weakly labeled data, as well as an additional data modality. I
describe my broad study of representation learning for speech data. Unlike most
other works that focus on a single learning setting, this thesis studies
multiple settings: supervised learning with auxiliary losses, unsupervised
learning, semi-supervised learning, and multi-view learning. Besides different
learning problems, I also explore multiple approaches for representation
learning. Though I focus on speech data, the methods described in this thesis
can also be applied to other domains. Overall, the field of representation
learning is developing rapidly. State-of-the-art results on speech related
tasks are typically based on Transformers pre-trained with large-scale
self-supervised learning, which aims to learn generic representations that can
benefit multiple downstream tasks. Since 2020, large-scale pre-training has
been the de facto choice to achieve good performance. This delayed thesis does
not attempt to summarize and compare with the latest results on speech
representation learning; instead, it presents a unique study on speech
representation learning before the Transformer era, that covers multiple
learning settings. Some of the findings in this thesis can still be useful
today.
