标题： 提示引导的轮流预测 显示英文标题

标题： Prompt-Guided Turn-Taking Prediction

摘要： 对话轮流预测模型是语音对话系统和对话机器人的关键组成部分。 最近的方法利用基于变压器的架构来连续且实时地预测语音活动。 在本研究中，我们提出了一种新模型，该模型可以通过文本提示动态控制对话轮流预测。 这种方法通过诸如“更快”或“更冷静”之类的指令实现直观且显式的控制，能够动态适应对话伙伴和语境。 所提出的模型建立在基于变压器的语音活动投影（VAP）模型之上，将文本提示嵌入到通道级变压器和跨通道变压器中。 我们使用超过950小时的人与人之间的语音对话数据评估了该方法的可行性。 由于现有数据集中没有适用于该方法的文本提示数据，我们使用了大型语言模型（LLM）生成合成提示句子。 实验结果表明，所提出的模型提高了预测准确性，并能根据文本提示有效改变对话轮流时间行为。 显示英文摘要

摘要： Turn-taking prediction models are essential components in spoken dialogue systems and conversational robots. Recent approaches leverage transformer-based architectures to predict speech activity continuously and in real-time. In this study, we propose a novel model that enables turn-taking prediction to be dynamically controlled via textual prompts. This approach allows intuitive and explicit control through instructions such as "faster" or "calmer" adapting dynamically to conversational partners and contexts. The proposed model builds upon a transformer-based voice activity projection (VAP) model, incorporating textual prompt embeddings into both channel-wise transformers and a cross-channel transformer. We evaluated the feasibility of our approach using over 950 hours of human-human spoken dialogue data. Since textual prompt data for the proposed approach was not available in existing datasets, we utilized a large language model (LLM) to generate synthetic prompt sentences. Experimental results demonstrated that the proposed model improved prediction accuracy and effectively varied turn-taking timing behaviors according to the textual prompts.