5 papers · 1 filter
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…
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…
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…