arXiv (All)
open access
Candor-LR: A Dyadic Conversational Dataset for Audio-Visual Speech Recognition
Current audio-visual speech recognition (AVSR) benchmarks, like LRS3, rely heavily on clean, scripted and rehearsed speech. They fail to reflect the complexity of natural conversation, which involves overlapping speech, spontaneous turn-taking, unscripted vocabulary and variable acoustic conditions.
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
- NAVIR: Neuromorphic Audio-Visual Speech Recognition for Robust Human-Robot Interaction on Edge Hardware#1050570
- Training-Free Speech-Centric Omni Understanding with Frozen VLMs#927543
- Personalized Korean Lipreading as Visual Speech Recognition: Transfer, Census and Adaptation on OLKAVS#1076644
- Assessing True Generalisability of Audio-Visual Speech Recognisers#1076902
- Voices as Handles: Reasoning about Speaker Identity with Frozen Text LLMs#1143102
Record · ID 997279
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