New pipeline enables spoken agents to act while listening

Researchers have developed a novel synthetic spoken-dialogue pipeline to address the latency challenges in spoken agents. This pipeline generates dialogues with annotations indicating the earliest point at which a tool and its arguments can be identified, enabling agents to act while listening rather than waiting for a full turn completion. The system constructs a corpus of 26,694 dialogues across 48 domains, totaling 786 hours, and includes an audio rendering of the $\tau^2$-bench for evaluation. An omni-modal language model trained on this data achieved tool-state macro-F1 scores of 0.48 and 0.47, demonstrating a significant lead time over traditional turn-end cascade methods. AI

IMPACT Enables spoken agents to respond faster by acting on partial transcripts, improving user experience in real-time applications.

RANK_REASON Academic paper detailing a new method and dataset for spoken dialogue systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• $au^2$-bench
• Heard Enough to Act: Early Tool Calling in Spoken Dialogue Systems
• Hugging Face
• omni-modal language model
• spoken agents
• Spoken Dialogue Systems
• tool calling

• paper
• infra

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