人类还是机器？一种语音到语音交互的初步图灵测试
Human or Machine? A Preliminary Turing Test for Speech-to-Speech Interaction
AI总结：
本文首次对语音到语音系统进行图灵测试，发现现有系统在类人特性上存在显著差距，提出细粒度分类法和可解释模型以提升对话AI的类人能力。
摘要：
追求类人对话代理的旅程长期以来一直受到图灵测试的指导。对于现代语音到语音（S2S）系统，一个关键但尚未回答的问题是它们是否能像人类一样对话。为此，我们进行了首次针对S2S系统的图灵测试，收集了2968个人对9种最先进的S2S系统与28名人类参与者之间对话的判断。我们的结果给出了一个明确的发现：现有的所有评估过的S2S系统都无法通过测试，揭示了在类人特性上的显著差距。为了诊断这一失败，我们开发了18个类人特性维度的细粒度分类法，并对所收集的对话进行众包标注。我们的分析显示，瓶颈并非语义理解，而是源于非语言特征、情感表达性和对话角色。此外，我们发现现成的AI模型在图灵测试中表现不可靠。为此，我们提出了一种可解释的模型，利用细粒度的类人特性评分，提供准确且透明的人类与机器区分，为自动类人特性评估提供了有力工具。我们的工作为S2S系统建立了首个类人特性评估，并超越二元结果，实现详细的诊断洞察，为对话AI系统的人类特性改进铺平了道路。
英文摘要：
The pursuit of human-like conversational agents has long been guided by the Turing test. For modern speech-to-speech (S2S) systems, a critical yet unanswered question is whether they can converse like humans. To tackle this, we conduct the first Turing test for S2S systems, collecting 2,968 human judgments on dialogues between 9 state-of-the-art S2S systems and 28 human participants. Our results deliver a clear finding: no existing evaluated S2S system passes the test, revealing a significant gap in human-likeness. To diagnose this failure, we develop a fine-grained taxonomy of 18 human-likeness dimensions and crowd-annotate our collected dialogues accordingly. Our analysis shows that the bottleneck is not semantic understanding but stems from paralinguistic features, emotional expressivity, and conversational persona. Furthermore, we find that off-the-shelf AI models perform unreliably as Turing test judges. In response, we propose an interpretable model that leverages the fine-grained human-likeness ratings and delivers accurate and transparent human-vs-machine discrimination, offering a powerful tool for automatic human-likeness evaluation. Our work establishes the first human-likeness evaluation for S2S systems and moves beyond binary outcomes to enable detailed diagnostic insights, paving the way for human-like improvements in conversational AI systems.
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。
