TurnNat：双人口语对话中轮流自然性的自动评估
TurnNat: Automatic Evaluation of Turn-Taking Naturalness in Dyadic Spoken Dialogue
AI总结：
提出TurnNat框架，通过因果轮流预测模型估计双说话人语音活动状态，利用负对数似然度量时序异常性，实现对话级轮流自然性自动评估。
摘要：
轮流自然性对于全双工口语对话系统至关重要，但其自动评估仍然有限。现有评估通常依赖人工判断或行为特定的时序指标，难以在统一框架内比较异质的时序失败。我们提出TurnNat，一种基于似然的双通道口语对话轮流自然性自动评估框架。一个在自然对话上训练的因果轮流预测模型估计未来双说话人语音活动状态，观测到的未来活动的负对数似然（NLL）度量时序异常性。TurnNat将帧级NLL汇集到从话语起始和偏移中提取的轮流边界单元（TBU）上，并聚合平均和尾部TBU分数得到对话级自然性分数。我们进一步构建了一个受控扰动基准，包含配对的自然和扰动对话片段，并通过人工自然性判断验证。在该基准上的实验表明，TurnNat能够成功识别跨异质时序失败的非自然轮流扰动。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
