12 citations · 23 across the 8 of their papers we have counts for
9 papers
COSI-Lab: Conference Living Lab for Modeling Multi-Perspective Multimodal Social Intention
Zonghuan Li, Litian Li, Arthur Mercier +17
COSI-Lab presents a multimodal multi-sensor dataset of a real interdisciplinary scientific workshop with 32 academics at an international conference. It captures ecologically valid…
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…
Dynamics of Collective Group Affect: Group-level Annotations and the Multimodal Modeling of Convergence and Divergence
Navin Raj Prabhu, Maria Tsfasman, Catharine Oertel +2
Collaborating in a group, whether face-to-face or virtually, involves continuously expressing emotions and interpreting those of other group members. Therefore, understanding group…
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…
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…
How unitizing affects annotation of cohesion
Eleonora Ceccaldi, Nale Lehmann-Willenbrock, Erica Volta +3
This paper investigates how unitizing affects external observers' annotation of group cohesion. We compared unitizing techniques belonging to these categories: interval coding, con…
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'…
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…
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…