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20212026
most citedEnd-to-End Label Uncertainty Modeling in Speech Emotion Recognition using Bayesian Neural Networks and Label Distribution Learning

12 citations · 23 across the 8 of their papers we have counts for

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eess.AS2025

Enhancing In-the-Wild Speech Emotion Conversion with Resynthesis-based Duration Modeling

Navin Raj Prabhu, Danilo de Oliveira, Nale Lehmann-Willenbrock +1

Speech Emotion Conversion aims to modify the emotion expressed in input speech while preserving lexical content and speaker identity. Recently, generative modeling approaches have…

eess.AS2023

EMOCONV-DIFF: Diffusion-based Speech Emotion Conversion for Non-parallel and In-the-wild Data

Navin Raj Prabhu, Bunlong Lay, Simon Welker +2

Speech emotion conversion is the task of converting the expressed emotion of a spoken utterance to a target emotion while preserving the lexical content and speaker identity. While…

eess.AS2023★ 1 cited

In-the-wild Speech Emotion Conversion Using Disentangled Self-Supervised Representations and Neural Vocoder-based Resynthesis

Navin Raj Prabhu, Nale Lehmann-Willenbrock, Timo Gerkmann

Speech emotion conversion aims to convert the expressed emotion of a spoken utterance to a target emotion while preserving the lexical information and the speaker's identity. In th…

eess.AS2022★ 12 cited

End-to-End Label Uncertainty Modeling in Speech Emotion Recognition using Bayesian Neural Networks and Label Distribution Learning

Navin Raj Prabhu, Nale Lehmann-Willenbrock, Timo Gerkman

To train machine learning algorithms to predict emotional expressions in terms of arousal and valence, annotated datasets are needed. However, as different people perceive others'…

eess.AS2022★ 1 cited

Label Uncertainty Modeling and Prediction for Speech Emotion Recognition using t-Distributions

Navin Raj Prabhu, Nale Lehmann-Willenbrock, Timo Gerkmann

As different people perceive others' emotional expressions differently, their annotation in terms of arousal and valence are per se subjective. To address this, these emotion annot…

eess.AS2021★ 2 cited

End-To-End Label Uncertainty Modeling for Speech-based Arousal Recognition Using Bayesian Neural Networks

Navin Raj Prabhu, Guillaume Carbajal, Nale Lehmann-Willenbrock +1

Emotions are subjective constructs. Recent end-to-end speech emotion recognition systems are typically agnostic to the subjective nature of emotions, despite their state-of-the-art…