arXiv (All)
open access
Learning Natural Conversational Behavior in Tandem Speech-to-Speech Models with Randomized Guidance
Tandem speech-to-speech architectures couple a responsive speech frontend with an asynchronous text backend. In KAME, a large language model (LLM) serves as the backend, supplying candidate responses as guidance to the speech frontend while the user is still speaking. Ordinary conversation recording
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
- Duplex-MPE: Benchmarking Multi-Party Interaction in Full-Duplex Dialogue#1115904
- The Rise of Verbal Reinforcement Learning#821618
- AdaptDuplex: from static to adaptive full-duplex spoken dialogue#1077146
- Mixture of Decoders for Diverse Dialog Response Generation#1155556
- Voice of Reason: Reinforcement Learning for Spoken Math#1012849
Record · ID 1105332
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