  In this paper we explore audiovisual emotion recognition under noisy acoustic
conditions with a focus on speech features. We attempt to answer the following
research questions: (i) How does speech emotion recognition perform on noisy
data? and (ii) To what extend does a multimodal approach improve the accuracy
and compensate for potential performance degradation at different noise levels?
We present an analytical investigation on two emotion datasets with
superimposed noise at different signal-to-noise ratios, comparing three types
of acoustic features. Visual features are incorporated with a hybrid fusion
approach: The first neural network layers are separate modality-specific ones,
followed by at least one shared layer before the final prediction. The results
show a significant performance decrease when a model trained on clean audio is
applied to noisy data and that the addition of visual features alleviates this
effect.
