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Robustness to environmental noise is important to creating automatic speechemotion recognition systems that are deployable in the real world. Prior workon noise robustness has assumed that systems would not make use ofsample-by-sample training noise conditions, or that they would have access tounlabelled testing data to generalize across noise conditions. We avoid theseassumptions and introduce the resulting task as heterogeneous conditiontraining. We show that with full knowledge of the test noise conditions, we canimprove performance by dynamically routing samples to specialized featureencoders for each noise condition, and with partial knowledge, we can use knownnoise conditions and domain adaptation algorithms to train systems thatgeneralize well to unseen noise conditions. We then extend these improvementsto the multimodal setting by dynamically routing samples to maintain temporalordering, resulting in significant improvements over approaches that do notspecialize or generalize based on noise type.

Dynamic Layer Customization for Noise Robust Speech Emotion Recognition in Heterogeneous Condition Training