<ul><li>Spontaneous speech emotion data often have uncertainty in labels due to grader opinion variation.</li><li>Using the probability density function of emotion grades as targets instead of consensus grades improves performance on benchmark evaluation sets.</li><li>Saliency-driven foundation model representation selection helps train a state-of-the-art speech emotion model for both dimensional and categorical emotion recognition.</li><li>Performance evaluation across multiple test-sets, along with analysis across gender and speakers, is necessary to assess the usefulness of emotion models.</li></ul>

Modeling speech emotion with label variance and analyzing performance across speakers and unseen acoustic conditions

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