Papers by Björn Schuller
Discovering and Causally Validating Emotion-Sensitive Neurons in Large Audio-Language Models (2026.acl-long)
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| Challenge: | Emotion is a central dimension of spoken communication, yet we lack a mechanistic account of how LALMs encode it internally. |
| Approach: | They propose to use emotion-sensitive neurons in large audio-language models to study their interpretations. |
| Outcome: | The proposed models show that they can be used to make decisions on emotion . the results show that the ESNs exhibit non-uniform clustering with partial cross-dataset transfer . |
Nkululeko: A Tool For Rapid Speaker Characteristics Detection (2022.lrec-1)
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| Challenge: | Nkululeko is a software tool that lets users perform semi-supervised machine learning experiments in the speaker characteristics domain. |
| Approach: | They propose a software tool called Nkululeko that lets users perform semi-supervised machine learning experiments in the speaker characteristics domain. |
| Outcome: | The proposed tool is based on audformat, a speech database metadata description . it supports best practise and fast setup of experiments without programming skills . |
A Comparative Cross Language View On Acted Databases Portraying Basic Emotions Utilising Machine Learning (2022.lrec-1)
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| Challenge: | Since several decades emotional databases have been recorded by various laboratories. |
| Approach: | They propose to model similarity as performance in cross database machine learning experiments and to analyze a manually picked set of four acoustic features that represent different phonetic areas. |
| Outcome: | The proposed sets of features represent different phonetic areas and are comparable across languages. |
Uncertainty Aware Review Hallucination for Science Article Classification (2021.findings-acl)
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Korbinian Friedl, Georgios Rizos, Lukas Stappen, Madina Hasan, Lucia Specia, Thomas Hain, Björn Schuller
| Challenge: | Existing approaches to peer review support are limited in their use of available information and subjectivity. |
| Approach: | They propose to use aleatory uncertainty and loss importance interpolations to model review representations at test time to provide a realistic evaluation framework. |
| Outcome: | The proposed framework makes better use of the available information and is realistic with respect to the limitations set by the task 1 . |
Modeling Emotional Trajectories in Written Stories Utilizing Transformers and Weakly-Supervised Learning (2024.findings-acl)
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| Challenge: | Existing work on how to model emotionality in stories has been limited to dictionary-based methods . |
| Approach: | They propose to introduce continuous valence and arousal labels for an existing dataset of children’s stories originally annotated with discrete emotion categories. |
| Outcome: | The proposed model achieves a Concordance Correlation Coefficient (CCC) of .8221 for valence and .7125 for arousal on the test set, demonstrating the efficacy of the proposed model. |