  Representations derived from models such as BERT (Bidirectional Encoder
Representations from Transformers) and HuBERT (Hidden units BERT), have helped
to achieve state-of-the-art performance in dimensional speech emotion
recognition. Despite their large dimensionality, and even though these
representations are not tailored for emotion recognition tasks, they are
frequently used to train large speech emotion models with high memory and
computational costs. In this work, we show that there exist lower-dimensional
subspaces within the these pre-trained representational spaces that offer a
reduction in downstream model complexity without sacrificing performance on
emotion estimation. In addition, we model label uncertainty in the form of
grader opinion variance, and demonstrate that such information can improve the
models generalization capacity and robustness. Finally, we compare the
robustness of the emotion models against acoustic degradations and observed
that the reduced dimensional representations were able to retain the
performance similar to the full-dimensional representations without significant
regression in dimensional emotion performance.
