Defining and Quantifying Conversation Quality in Spontaneous Interactions. - Navin Raj Prabhu; Chirag Raman; Hayley Hung (ICMI Companion, 2020)

End-to-end label uncertainty modeling for speech emotion recognition using Bayesian neural networks. - Navin Raj Prabhu; Guillaume Carbajal; Nale Lehmann-Willenbrock; Timo Gerkmann (CoRR, 2021)

Label Uncertainty Modeling and Prediction for Speech Emotion Recognition using t-Distributions. - Navin Raj Prabhu; Nale Lehmann-Willenbrock; Timo Gerkmann (ACII, 2022)

End-To-End Label Uncertainty Modeling for Speech-based Arousal Recognition Using Bayesian Neural Networks. - Navin Raj Prabhu; Guillaume Carbajal; Nale Lehmann-Willenbrock; Timo Gerkmann (INTERSPEECH, 2022)

Leveraging Semantic Information for Efficient Self-Supervised Emotion Recognition with Audio-Textual Distilled Models. - Danilo de Oliveira; Navin Raj Prabhu; Timo Gerkmann (INTERSPEECH, 2023)

In-the-wild Speech Emotion Conversion Using Disentangled Self-Supervised Representations and Neural Vocoder-based Resynthesis. - Navin Raj Prabhu; Nale Lehmann-Willenbrock; Timo Gerkmann (CoRR, 2023)

Perceived Conversation Quality in Spontaneous Interactions. - Chirag Raman; Navin Raj Prabhu; Hayley Hung (IEEE Trans. Affect. Comput., 2023)

EMOCONV-Diff: Diffusion-Based Speech Emotion Conversion for Non-Parallel and in-the-Wild Data. - Navin Raj Prabhu; Bunlong Lay; Simon Welker; Nale Lehmann-Willenbrock; Timo Gerkmann (ICASSP, 2024)

Dynamics of Collective Group Affect: Group-level Annotations and the Multimodal Modeling of Convergence and Divergence. - Navin Raj Prabhu; Maria Tsfasman; Catharine Oertel; Timo Gerkmann; Nale Lehmann-Willenbrock (CoRR, 2024)

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 Gerkmann (IEEE Trans. Affect. Comput., 2024)