JAL-Turn: Joint Acoustic-Linguistic Modeling for Real-Time and Robust Turn-Taking Detection in Full-Duplex Spoken Dialogue Systems
Efficient and robust turn-taking detection remains a significant challenge in industrial-grade voice AI agents. Existing systems often rely on acoustic or linguistic cues alone, limiting accuracy and stability, whereas directly endowing large language models with full-duplex capability incurs heavy data and deployment costs that hinder real-time usage. We propose JAL-Turn, a lightweight speech-only turn-taking framework based on a joint acoustic-linguistic modeling paradigm, in which a cross-attention module adaptively fuses pre-trained acoustic and linguistic representations for low-latency hold or shift prediction. By sharing a frozen ASR encoder, JAL-Turn enables turn-taking prediction...
| Authors | Yu Pan, Guangzhao Yang, Muhammad Huzaifah et al. |
|---|---|
| Primary category | cs.CL |
| Published | 27 March 2026 |
| Last updated at source | 12 October 2026 |
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