  We examine the use of linear and non-linear dimensionality reduction
algorithms for extracting low-rank feature representations for speech emotion
recognition. Two feature sets are used, one based on low-level descriptors and
their aggregations (IS10) and one modeling recurrence dynamics of speech (RQA),
as well as their fusion. We report speech emotion recognition (SER) results for
learned representations on two databases using different classification
methods. Classification with low-dimensional representations yields performance
improvement in a variety of settings. This indicates that dimensionality
reduction is an effective way to combat the curse of dimensionality for SER.
Visualization of features in two dimensions provides insight into
discriminatory abilities of reduced feature sets.

Machine Learning