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Found 5 papers, 3 papers with code
EMOCONV-DIFF: Diffusion-based Speech Emotion Conversion for Non-parallel and In-the-wild Data
Subsequently, at inference, a target emotion embedding is employed to convert the emotion of the input utterance to the given target emotion.
In-the-wild Speech Emotion Conversion Using Disentangled Self-Supervised Representations and Neural Vocoder-based Resynthesis
In this work, we specifically focus on in-the-wild emotion conversion where parallel data does not exist, and the problem of disentangling lexical, speaker, and emotion information arises.
End-to-End Label Uncertainty Modeling in Speech Emotion Recognition using Bayesian Neural Networks and Label Distribution Learning
Instead of a Gaussian, we model the annotation distribution using Student's t-distribution, which also accounts for the number of annotations available.
Label Uncertainty Modeling and Prediction for Speech Emotion Recognition using t-Distributions
To address this, these emotion annotations are typically collected by multiple annotators and averaged across annotators in order to obtain labels for arousal and valence.
End-To-End Label Uncertainty Modeling for Speech-based Arousal Recognition Using Bayesian Neural Networks
At training, the network learns a distribution of weights to capture the inherent uncertainty related to subjective arousal annotations.
