  Dimensional representations of speech emotions such as the arousal-valence
(AV) representation provide a continuous and fine-grained description and
control than their categorical counterparts. They have wide applications in
tasks such as dynamic emotion understanding and expressive text-to-speech
synthesis. Existing methods that predict the dimensional emotion representation
from speech cast it as a supervised regression task. These methods face data
scarcity issues, as dimensional annotations are much harder to acquire than
categorical labels. In this work, we propose to learn the AV representation
from categorical emotion labels of speech. We start by learning a rich and
emotion-relevant high-dimensional speech feature representation using
self-supervised pre-training and emotion classification fine-tuning. This
representation is then mapped to the 2D AV space according to psychological
findings through anchored dimensionality reduction. Experiments show that our
method achieves a Concordance Correlation Coefficient (CCC) performance
comparable to state-of-the-art supervised regression methods on IEMOCAP without
leveraging ground-truth AV annotations during training. This validates our
proposed approach on AV prediction. Furthermore, visualization of AV
predictions on MEAD and EmoDB datasets shows the interpretability of the
learned AV representations.
