1 citations · 2 across the 11 of their papers we have counts for
12 papers
Concentrated Causal Influence, Distributed Diagnostic Information: Cognitive Impairment Decisions in Large Audio-Language Models
Xiutian Zhao, Luqi Sun, Björn Schuller +1
Large audio-language models (LALMs) can assess cognitive impairment (CI) from speech, yet their answers reveal neither which internal components control the decision nor how much C…
Perceptible or Not? Diagnosing Passive Fingerprints for Speech Deepfake Attribution
Yupei Li, Qiyang Sun, Emmanouil Benetos +2
Passive fingerprints (intrinsic traces naturally left by generators) have been shown to enable attribution in speech deepfake detection, yet their persistence, reproducibility, and…
Emotion Across Speech and Faces: Shared Affective Mechanisms in Multimodal Foundation Models
Xiutian Zhao, Luqi Sun, Björn Schuller +1
Modern multimodal foundation models (MFMs) have made rapid progress on tasks requiring integrated perception across speech, vision, and language, including emotion recognition. How…
Multilingual Emotion Neurons in Large Audio-Language Models
Xiutian Zhao, Philipp Koehn, Björn Schuller +1
Emotion is central to human communication, and its expression varies across languages. Large audio-language models (LALMs) achieve strong performance on multilingual speech tasks,…
Feature-Augmented Transformers for Robust AI-Text Detection Across Domains and Generators
Mohamed Mady, Johannes Reschke, Björn Schuller
AI-generated text is nowadays produced at scale across domains and heterogeneous generation pipelines, making robustness to distribution shift a central requirement for supervised…
Explainable Speech Emotion Recognition: Weighted Attribute Fairness to Model Demographic Contributions to Social Bias
Tomisin Ogunnubi, Yupei Li, Björn Schuller
Speech Emotion Recognition (SER) systems have growing applications in sensitive domains such as mental health and education, where biased predictions can cause harm. Traditional fa…
Neuron-Level Emotion Control in Speech-Generative Large Audio-Language Models
Xiutian Zhao, Ismail Rasim Ulgen, Philipp Koehn +2
Large audio-language models (LALMs) can produce expressive speech, yet reliable emotion control remains elusive: conversions often miss the target affect and may degrade linguistic…
Discovering and Causally Validating Emotion-Sensitive Neurons in Large Audio-Language Models
Xiutian Zhao, Björn Schuller, Berrak Sisman
Emotion is a central dimension of spoken communication, yet, we still lack a mechanistic account of how modern large audio-language models (LALMs) encode it internally. We present…
DFALLM: Achieving Generalizable Multitask Deepfake Detection by Optimizing Audio LLM Components
Yupei Li, Li Wang, Yuxiang Wang +5
Audio deepfake detection has recently garnered public concern due to its implications for security and reliability. Traditional deep learning methods have been widely applied to th…
The Affective Bridge: Preserving Speech Representations while Enhancing Deepfake Detection via emotional Constraints
Yupei Li, Chenyang Lyu, Longyue Wang +3
Speech deepfake detection (DFD) has benefited from diverse acoustic and semantic speech representations, many of which encode valuable speech information and are costly to train. P…
On Prompt Sensitivity of ChatGPT in Affective Computing
Mostafa M. Amin, Björn W. Schuller
Recent studies have demonstrated the emerging capabilities of foundation models like ChatGPT in several fields, including affective computing. However, accessing these emerging cap…
Accurate Emotion Strength Assessment for Seen and Unseen Speech Based on Data-Driven Deep Learning
Rui Liu, Berrak Sisman, Björn Schuller +2
Emotion classification of speech and assessment of the emotion strength are required in applications such as emotional text-to-speech and voice conversion. The emotion attribute ra…