arXiv (All)
open access
$S^3$-Bench: Evaluating Speech Interaction Models as Scientific Voice Assistants
The advance of multimodal large language models (MLLMs) has fundamentally reshaped the paradigm of human-computer interaction, especially speech interaction models capable of seamless conversations. Despite remarkable performance as general voice assistants, their performance in specialized domains
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
- MP-Bench: Evaluating Voice Agents as a Multiparty Conversation Participant#1070577
- Human-Centric Image Captioning with Subject-Centered Spatial Understanding#985668
- TAKE 85: Testing Audiovisual filmmaKer's intEnt across 85 Hours of Film#925247
- TAKE 85: Testing Audiovisual filmmaKer's intEnt across 85 Hours of Film#921011
- What Did the MLLM Hear? Token-Level Spectro-Temporal Grounding for Audio MLLM Explainability#1070148
Record · ID 996669
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