M3-DuplexBench：面向全双工口语对话模型的多轮、多语言、多领域基准测试集
M3-DuplexBench: A Multi-Turn, Multilingual, Multidomain Benchmark for Full-Duplex Spoken Dialogue Models
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中文总结 AI 辅助
研究针对全双工口语对话系统多轮公平比较难、现有基准覆盖不足的问题，提出支持英日双语、涵盖两类对话场景的M3-DuplexBench基准，通过多设置实验揭示模型话轮特征与性能差异。
中文摘要 AI 辅助
全双工口语对话系统（FDSDS）可在说话时监听，实现流畅话轮转换、反馈处理、用户插话处理等自然行为。但多轮对话中的公平比较仍是挑战，现有基准对语言和对话领域的覆盖有限。我们提出M3-DuplexBench，这是面向FDSDS的多轮、多语言、多领域基准测试集，支持英语和日语，涵盖日常对话与多轮问答。此外，我们在单轮、仅用户、教师强制全上下文等多种对话上下文设置下评估模型，分析对话历史对模型行为的影响。对近期FDSDS的实验显示，模型具有特定的话轮转换特征，不同语言和领域间存在明显性能差距，对话上下文的影响则各不相同。
英文摘要
Full-duplex spoken dialogue systems (FDSDSs) can listen while speaking, enabling natural behaviors such as smooth turn-taking, backchannel handling, and user barge-in handling. However, fair comparisons in multi-turn conversations remain a challenge. In addition, existing benchmarks provide limited coverage of languages and dialogue domains. We propose M3-DuplexBench, a multi-turn, multilingual, multidomain benchmark for FDSDSs. M3-DuplexBench supports English and Japanese and covers both casual conversation and multi-turn question answering. In addition, we evaluate models under multiple dialogue context settings, including single-turn, user-only, and teacher-forced full-context settings, to analyze how dialogue history affects model behavior. Experiments with recent FDSDSs reveal model-specific turn-taking characteristics, clear performance gaps across languages and domains, and mixed effects of dialogue context.
