arXiv (All)
open access
Zero-Shot Cue-Grounded Topic Segmentation of Spoken Documents
Topic segmentation structures spoken documents into coherent sections, facilitating navigation and downstream understanding. The appropriate granularity can vary substantially, ranging from broad thematic shifts to fine-grained subtopics. Existing LLM-based segmenters, however, often struggle to ada
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- BanglaTurn: A Benchmark and Whisper-Based Model for End-of-Turn Detection in Bangla Speech#1077300
- Teaching LLMs to Hear Who Spoke What: Metadata-Supervised Pretraining for Encoder-Free Speech-LLMs#1156966
- Event segmentation during speech listening: The relative contributions of thematic structure and prosody#31115
- Dialogue-Based Streaming Audio-Visual Target Speaker Extraction with Predictive Dialogue Information#1105333
- MusTBench: Benchmarking and Advancing Temporal Grounding in Music LLMs#821455
Record · ID 1128696
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive