  Human emotional speech is, by its very nature, a variant signal. This results
in dynamics intrinsic to automatic emotion classification based on speech. In
this work, we explore a spectral decomposition method stemming from
fluid-dynamics, known as Dynamic Mode Decomposition (DMD), to computationally
represent and analyze the global utterance-level dynamics of emotional speech.
Specifically, segment-level emotion-specific representations are first learned
through an Emotion Distillation process. This forms a multi-dimensional signal
of emotion flow for each utterance, called Emotion Profiles (EPs). The DMD
algorithm is then applied to the resultant EPs to capture the eigenfrequencies,
and hence the fundamental transition dynamics of the emotion flow. Evaluation
experiments using the proposed approach, which we call EigenEmo, show promising
results. Moreover, due to the positive combination of their complementary
properties, concatenating the utterance representations generated by EigenEmo
with simple EPs averaging yields noticeable gains.
