TurnGuide: Enhancing Meaningful Full Duplex Spoken Interactions via Dynamic Turn-Level Text-Speech Interleaving
arXiv:2508.07375v3 Announce Type: replace Abstract: Full-Duplex Speech Language Models (FD-SLMs) are specialized foundation models designed to enable natural, real-time spoken interactions by modeling complex conversational turn-taking such as interruptions, backchannels, and overlapping speech. End-to-end (e2e) FD-SLMs leverage real-world double-channel conversational data to capture nuanced two-speaker dialogue patterns for human-like interactions, but their conversational abilities often degrade compared to pure-text conversation due to prolonged speech sequences and limited high-quality sp
The continuous drive towards more natural and human-like AI interactions is pushing the boundaries of conversational AI, particularly in multimodal domains combining text and speech.
Improving the naturalness and efficiency of human-AI spoken interactions can significantly broaden the applicability and adoption of AI systems in various real-world scenarios.
This advancement enables AI to engage in more sophisticated, real-time, and interruption-tolerant spoken dialogues, bridging the gap between text-based and truly conversational AI.
- · AI developers
- · Speech technology companies
- · Customer service industries
- · Virtual assistant providers
- · AI systems with poor conversational capabilities
- · Companies relying solely on rigid, turn-based spoken interfaces
AI models will achieve more fluid and human-like spoken conversations.
This improved interaction quality will lead to wider deployment of AI in critical, real-time communication roles previously limited by technology.
The enhanced naturalness of AI-human speech could accelerate the integration of AI agents into daily life, blurring the lines between human and artificial interlocutors.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL