  Learning disentangled representations of high-dimensional data is currently
an active research area. However, compared to the field of computer vision,
less work has been done for speech processing. In this paper, we provide a
review of two representative efforts on this topic and propose the novel
concept of fine-grained disentangled speech representation learning.
