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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
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