JAL-Turn: Joint Acoustic-Linguistic Modeling for Real-Time and Robust Turn-Taking Detection in Full-Duplex Spoken Dialogue Systems
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The paper introduces a decoupled data approach for the Neural Finite State Machine (NFSM) framework to improve full‑duplex dialogue. It serializes real human‑human spoken dialogues into FSM tapes using a rule‑based event‑guided transformation, while shaping semantics through human‑agent text dialogues. A Source‑Aware Calibrated (SAC) loss is proposed to balance state‑transition token distribution and align each data source with its strongest supervisory signal, leading to better turn‑taking performance without sacrificing semantic quality.
TurnBench is a new multi‑domain benchmark for evaluating turn‑taking dynamics in spoken dialogue. It comprises a 30‑hour hand‑labeled corpus of dyadic human conversations, a standardized evaluation protocol for end‑of‑turn and interruption detection, and covers six distinct interaction styles with triple annotation. The benchmark also provides a 104‑hour training set, a public leaderboard, and an interactive dataset viewer at https://turnbench.sesame.com.
The study investigates which speech aspects best signal the end of a speaker’s turn in conversational AI. By systematically ablating acoustic, prosodic, and semantic cues in a lightweight trimodal classifier, the authors find that combining acoustic and prosodic features yields the highest accuracy and lowest latency, achieving an utterance F1 of 0.93 with 7.8% false alarms at 400 ms median latency. Adding textual information actually increases premature detections without improving performance, and feature analysis shows prosodic cues provide the strongest class separability while text representations overlap significantly.
M3-DuplexBench is a new benchmark designed to evaluate full‑duplex spoken dialogue systems (FDSDSs) that can listen while speaking. It covers multi‑turn interactions in both English and Japanese across casual conversation and question‑answering domains, jointly assessing interaction timing and response content. The benchmark also offers a reference‑conditioned setting to test sensitivity to dialogue history and trajectory mismatch.
arXiv:2609.13076v1 Announce Type: cross Abstract: Conversational voice agents have advanced significantly, offering increasingly natural human-machine interactions through both cascaded and end-to-en...
arXiv:2608.28630v1 Announce Type: cross Abstract: Compared with half-duplex dialogue systems where the system waits for user turn completion before it responds, natural full-duplex dialogue systems r...