11 citations · 11 across the 9 of their papers we have counts for
6 papers · 1 filter
Calibrating LLM Judges for Human and AI Conversations
Maike Züfle, Patrícia Schmidtová, Vilém Zouhar +9
Measuring how successful a conversation is remains difficult, even for humans judging spoken dialogue. We evaluate state-of-the-art LLMs as pointwise and pairwise judges of convers…
Controlling Backchannels in Streamable Full-duplex Models
Maike Züfle, Peter Polák, Sefik Emre Eskimez +3
Backchannels, brief acknowledgements like "uh-huh" produced while the other party may still be talking, are central to natural conversation, but full-duplex spoken dialogue models…
Distributional Metrics for Evaluating Spoken Conversational Systems
Shree Harsha Bokkahalli Satish, Erica Cooper, Patrícia Schmidtová +4
Evaluating conversational systems is a difficult and unresolved problem. We introduce the Conversational Distribution Score (CDS), which compares distributions of conversational be…
Why We Need Speech to Evaluate Speech Translation
Maike Züfle, Danni Liu, Vilém Zouhar +1
Speech translation models are increasingly capable of preserving speech-specific information (e.g., speaker gender, prosody, and emphasis), yet evaluation metrics remain blind to s…
Do What I Say: A Spoken Prompt Dataset for Instruction-Following
Maike Züfle, Sara Papi, Fabian Retkowski +5
Speech Large Language Models (SLLMs) have rapidly expanded, supporting a wide range of tasks. These models are typically evaluated using text prompts, which may not reflect real-wo…
F-Actor: Controllable Conversational Behaviour in Full-Duplex Models
Maike Züfle, Ondrej Klejch, Nicholas Sanders +3
Spoken conversational systems require more than accurate speech generation to have human-like conversations: to feel natural and engaging, they must produce conversational behaviou…