1 citations · 1 across the 11 of their papers we have counts for
11 papers
SCoPE: Shift-Aware Speaker-Conditioned Priors for Emotion Recognition in Conversations
Burak Can Kaplan, Stefan Wermter
In conversations, human emotions are transient; however, they tend to persist across multiple utterances. For example, we rarely switch instantly between contrasting emotions such…
Identifying the Unknown: Prompt-Free Open Vocabulary Anomaly Recognition for Robot-Object Interaction
Philipp Allgeuer, Jan-Gerrit Habekost, Stefan Wermter
Robots operating in real-world environments must in general be able to recognize previously unseen objects. As robotic systems move toward open-world autonomy, there is a growing,…
Extraction and Analysis of Multimodal Concepts in Vision Language Models through Sparse Autoencoders
Sergio Lanza, Jae Hee Lee, Stefan Wermter
Vision Language Models (VLMs) have demonstrated impressive performance in tasks requiring joint understanding of images and text, such as image captioning and Visual Question Answe…
A Generalized Formalism of Auto-Regressive Decoding for Speech Processing
Julia Gachot, Philipp Allgeuer, Marie S. Bauer +1
In speech processing, most state-of-the-art sequence prediction models rely on auto-regressive (AR) strategies to generate output sequences based on the raw predictions of the mode…
MoDiCoL: A Modular Diagnostic Continual Learning Dataset for Robust Speech Recognition
Theresa Pekarek Rosin, Matthias Kerzel, Stefan Wermter
Modern Automatic Speech Recognition (ASR) systems have made remarkable progress on standard benchmarks, yet performance gaps have emerged under real-world distribution shifts, caus…
Learning to Hear Hesitation: Continual Learning for Disfluency-Aware ASR
Henri-Leon Kordt, Theresa Pekarek Rosin, Jae Hee Lee +1
Despite advances in large-scale Automatic Speech Recognition (ASR), disfluent speech remains challenging, as state-of-the-art systems are often optimized to omit disfluencies, lead…
The Expert Strikes Back: Interpreting Mixture-of-Experts Language Models at Expert Level
Jeremy Herbst, Stefan Wermter, Jae Hee Lee
Mixture-of-Experts (MoE) architectures have become the dominant choice for scaling Large Language Models (LLMs), activating only a subset of parameters per token. While MoE archite…
Prompt-to-Gesture: Measuring the Capabilities of Image-to-Video Deictic Gesture Generation
Hassan Ali, Doreen Jirak, Luca Müller +1
Gesture recognition research, unlike NLP, continues to face acute data scarcity, with progress constrained by the need for costly human recordings or image processing approaches th…
Large Language Model Data Generation for Enhanced Intent Recognition in German Speech
Theresa Pekarek Rosin, Burak Can Kaplan, Stefan Wermter
Intent recognition (IR) for speech commands is essential for artificial intelligence (AI) assistant systems; however, most existing approaches are limited to short commands and are…
Can Large Language Models Generate Effective Datasets for Emotion Recognition in Conversations?
Burak Can Kaplan, Hugo Cesar De Castro Carneiro, Stefan Wermter
Emotion recognition in conversations (ERC) focuses on identifying emotion shifts within interactions, representing a significant step toward advancing machine intelligence. However…
FabuLight-ASD: Unveiling Speech Activity via Body Language
Hugo Carneiro, Stefan Wermter
Active speaker detection (ASD) in multimodal environments is crucial for various applications, from video conferencing to human-robot interaction. This paper introduces FabuLight-A…