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In applications that use emotion recognition via speech, frame-loss can be asevere issue given manifold applications, where the audio stream loses somedata 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 performanceof emotion recognition via speech. Reproducible extensive experiments arereported on the popular RECOLA corpus using a state-of-the-art end-to-end deepneural network, which mainly consists of convolution blocks and recurrentlayers. A simple environment based on a Markov Chain model is used to model theloss mechanism based on two main parameters. We explore matched, mismatched,and multi-condition training settings. As one expects, the matched settingyields the best performance, while the mismatched yields the lowest.Furthermore, frame-loss as a data augmentation technique is introduced as ageneral-purpose strategy to overcome the effects of frame-loss. It can be usedduring training, and we observed it to produce models that are more robustagainst frame-loss in run-time environments.

"I have vxxx bxx connexxxn!": Facing Packet Loss in Deep Speech Emotion Recognition