  In applications that use emotion recognition via speech, frame-loss can be a
severe issue given manifold applications, where the audio stream loses some
data frames, for a variety of reasons like low bandwidth. In this contribution,
we investigate for the first time the effects of frame-loss on the performance
of emotion recognition via speech. Reproducible extensive experiments are
reported on the popular RECOLA corpus using a state-of-the-art end-to-end deep
neural network, which mainly consists of convolution blocks and recurrent
layers. A simple environment based on a Markov Chain model is used to model the
loss mechanism based on two main parameters. We explore matched, mismatched,
and multi-condition training settings. As one expects, the matched setting
yields the best performance, while the mismatched yields the lowest.
Furthermore, frame-loss as a data augmentation technique is introduced as a
general-purpose strategy to overcome the effects of frame-loss. It can be used
during training, and we observed it to produce models that are more robust
against frame-loss in run-time environments.
