TurnNat: Automatic Evaluation of Turn-Taking Naturalness in Dyadic Spoken Dialogue
TurnNat is a likelihood-based framework for automatic evaluation of turn-taking naturalness in dyadic spoken dialogue. It uses a causal prediction model to compute negative log-likelihood of future voice activity, quantifying timing atypicality, and demonstrates effectiveness on a perturbation benchmark.
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[Submitted on 1 Jul 2026]
Title:TurnNat: Automatic Evaluation of Turn-Taking Naturalness in Dyadic Spoken Dialogue
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Abstract:Turn-taking naturalness is central to full-duplex spoken dialogue systems, yet its automatic evaluation remains limited. Existing evaluations often rely on human judgments or behavior-specific timing metrics, making it difficult to compare heterogeneous timing failures within a unified framework. We propose TurnNat, a likelihood-based framework for automatic turn-taking naturalness evaluation in two-channel spoken dialogue. A causal turn-taking prediction model trained on natural conversations estimates future two-speaker voice-activity states, and the negative log-likelihood (NLL) of the observed future activity measures timing atypicality. TurnNat pools frame-level NLLs over turn-taking boundary units (TBUs) extracted from utterance onsets and offsets, and aggregates mean and tail TBU scores into a dialogue-level naturalness score. We further construct a controlled perturbation benchmark of paired natural and perturbed dialogue clips, validated by human naturalness judgments. Experiments on this benchmark show that TurnNat successfully identifies unnatural turn-taking perturbations across heterogeneous timing failures.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.01345 [cs.CL]
(or arXiv:2607.01345v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.01345
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hao Zhang [view email] [v1] Wed, 1 Jul 2026 18:03:09 UTC (1,135 KB)
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