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    Home»Development»Machine Learning»Modeling Speech Emotion With Label Variance and Analyzing Performance Across Speakers and Unseen Acoustic Conditions

    Modeling Speech Emotion With Label Variance and Analyzing Performance Across Speakers and Unseen Acoustic Conditions

    April 2, 2025

    Spontaneous speech emotion data usually contain perceptual grades where graders assign emotion score after listening to the speech files. Such perceptual grades introduce uncertainty in labels due to grader opinion variation. Grader variation is addressed by using consensus grades as groundtruth, where the emotion with the highest vote is selected, and as a consequence fails to consider ambiguous instances where a speech sample may contain multiple emotions, as captured through grader opinion uncertainty. We demonstrate that using the probability density function of the emotion grades as…

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