  Despite recent strides made in Speech Separation, most models are trained on
datasets with neutral emotions. Emotional speech has been known to degrade
performance of models in a variety of speech tasks, which reduces the
effectiveness of these models when deployed in real-world scenarios. In this
paper we perform analysis to differentiate the performance degradation arising
from the emotions in speech from the impact of out-of-domain inference. This is
measured using a carefully designed test dataset, Emo2Mix, consisting of
balanced data across all emotional combinations. We show that even models with
strong out-of-domain performance such as Sepformer can still suffer significant
degradation of up to 5.1 dB SI-SDRi on mixtures with strong emotions. This
demonstrates the importance of accounting for emotions in real-world speech
separation applications.
