arxiv
PublishedOctober 31, 2026 at 4:00 AM
The Importance of Speech Genre for End-of-Turn Detector Training
Publisher summary· verbatim
arXiv:2610.39742v1 Announce Type: cross Abstract: End-of-turn (EoT) detection has been studied mostly through features, models, and modalities, while the speech genre of the training data has drawn far less attention. We isolate this factor. Using one reproducible annotation pipeline, we build a mat
Stay posted· Newsletter
Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.
Your email is used only to send The Bubble Brief. Privacy policy
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivWhat Does an Ontology Catch? Curated, LLM-Generated and Data-Derived Ontologies as Checkers of LLM Agents over Knowledge Graphs3harxivGAUSS: A Workload-Aware Analytical and Monte-Carlo Simulator for LLM Inference Serving3harxivAlgorithmic Bias from the Perspectives of Healthcare Professionals3harxivEvaluating Causal Reasoning in LLMs: A Case Study in the Challenges of Procedural Generation3hThe Bubble Brief
MON · WED · FRIRead AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.
Your email is used only to send The Bubble Brief. Privacy policy
Originally published on arxiv ↗