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TurnTurk-GPT is a text-based turn-taking predictor for Turkish, created by fine-tuning a Turkish GPT-2 model with the TurnGPT objective, which outperforms multilingual end-of-turn detectors and locates Turkish turn-final cues in verb-final morphology.
论文提出"主动性向量"（initiative vector），从全双工语音语言模型自身的残差流中无需训练即可提取的单一方向，用于读取和调控模型的抢话/让话决策，并通过参考录音实现说话人轮替风格的迁移。
实践者分享构建电话语音AI代理的核心瓶颈并非模型能力，而是轮次切换时机：单靠VAD静音检测会导致打断或迟钝，改用STT置信度与部分转录文本的语义线索来判断发言是否结束效果更好。
发布开源权重的 ASR 模型 TYGER，集成全双工轮流控制，可检测用户说完、打断及语气词，中位端点延迟 250 毫秒；配套语音 Agent tyger-s2s 仅需一块 32 GB GPU 即可运行最多 4 路对话。
该论文提出 PeopleMakingConversation 基准，通过 176 段非脚本对话中的 427 个停顿点和 482 个话轮结束点，评估六个实时语音系统在何时开口、延迟、回复长度与对话记忆等方面的自然对话表现，并公开数据与评测框架。
A practitioner reflection on why voice agents cut callers off, arguing the real culprit is often VAD settings misreading background noise rather than STT latency, and that tuning silence thresholds changes how human an agent feels.
作者讨论了 JEV 这类决策模型在 VoiceAgent 中的新可能性，认为其在轮次切换检测上非常精准，并列举了若支持判断用户是否说完、是否打断、情绪识别、是否拒绝识别及是否调用工具等能力，可显著提升语音助手体验，甚至无需端到端模型。
这篇论文提出了一套自动化流程，用于从分离声道录音的自发对话中提取对话轮次与反馈语（backchannels），在99段丹麦语对话上评估得到约0.62的F1分数，可作为半自动标注流程中的一致性初步标注。
作者分享为电话语音机器人调优对话轮替（turn-taking）的经验：单靠 VAD 静音阈值不够，需结合 STT 部分文本判断句子是否完整，再配合 TTS 延迟一起调试，才能让机器人响应更自然。
FD-VAD proposes an ASR-free, streaming semantic endpointer that maps causal audio windows directly to Continue/Stop decisions for full-duplex voice agents, combining a frozen speech encoder with a parameter-efficiently adapted language model and achieving state-of-the-art EOT recall on TurnBench.
This paper introduces a lightweight backchannel head for full-duplex spoken dialogue models to predict and control the timing of backchannels, improving natural conversation dynamics.
A developer describes a failure mode in AI phone agents where the agent stalls mid-call without errors, causing dropped calls and turn-taking issues, and seeks diagnostic advice.
The paper introduces TACT, a benchmark for evaluating turn-taking in full-duplex spoken dialogue models that uses intent-conditioned continuous scoring to replace binary rules, demonstrating better alignment with human judgments.
This paper evaluates full-duplex speech models' ability to decide when to speak, finding that models like Moshi and PersonaPlex primarily respond to being addressed or silence rather than content-driven triggers such as false claims or hazards, identifying a gap in content understanding.
TurnBench introduces a multi-domain benchmark for assessing turn-taking dynamics in spoken dialogue, featuring a hand-labeled corpus and standardized evaluation protocols for end-of-turn and interruption detection.
This paper proposes using semantic uncertainty derived from large language models to anticipate transition relevance places in spoken turn-taking, showing improved performance over baselines in dialogue systems.
This paper proposes a decoupled data approach to improve turn-taking in full-duplex dialogue by learning from real spoken dialogues while using text for semantics, leveraging a neural finite state machine framework to enhance naturalness and preserve semantic capabilities.
This paper proposes a multi-task learning approach that leverages turn-taking dynamics to enhance intent recognition in multi-party conversations, outperforming existing methods that ignore interaction patterns.
An observation about how AI voice agents struggle with realistic customer behavior such as interruptions, self-corrections, and mid-sentence changes, suggesting turn-taking is a key challenge for enterprise voice AI.
This paper introduces X2-Turn, a frame-synchronous dual-head model that jointly performs streaming ASR and turn state prediction on shared representations, improving turn-taking accuracy and latency in spoken dialogue systems.