DuplexGen：人机轮换对话的自适应合成

摘要

话轮转换是全双工交互的核心组成部分。何种话轮转换行为恰当因场景而异，然而当前模型无论上下文如何均采用单一规范。这一局限源于其训练数据：人人对话语音语料库捕捉了自然时序现象，但缺乏角色定位或场景特定规范；而基于启发式或提示的合成方法注入话轮转换行为时，并未以人类偏好为依据。我们提出DuplexGen框架，通过针对少量槽位级人类偏好标注校准大语言模型预测，生成具有场景自适应话轮转换的对话。在六项合作与竞争性任务中，人类的话轮转换偏好存在系统性差异，而DuplexGen与这些偏好的契合度远高于未校准的提示方法或仅基于通用人人对话数据训练的方法；基于DuplexGen生成数据训练的全双工模型展现出明显符合人类偏好的独特话轮转换行为。这些结果表明，使话轮转换合成具备场景特异性所依赖的是人工校准，而非仅靠语料库规模或提示设计。

English

Turn-taking is a central component of full-duplex interaction. Which turn-taking behaviors are appropriate varies with the scenario, yet current models apply a single norm regardless of context. This limitation originates in their training data: human-human speech corpora capture natural timing phenomena but provide little role grounding or scenario-specific norms, while heuristic or prompted synthesis methods inject turn-taking behaviors without basing them on human preferences. We introduce DuplexGen, a framework for generating dialogues with scenario-adaptive turn-taking by calibrating LLM predictions against a small set of slot-level human preference annotations. In six cooperative and competitive tasks, human turn-taking preferences differ systematically, and DuplexGen aligns substantially more closely with those preferences than uncalibrated prompting or training solely on generic human-human data; a full-duplex model trained on DuplexGen-generated data exhibits distinctive, human-preferred turn-taking behaviors. These results show that human calibration, not corpus scale or prompt design alone, is what allows turn-taking synthesis to be scenario-specific.