发表机构
Columbia University; Instagram（哥伦比亚大学; 照片墙）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究代码切换语音设置中人类与分类模型夹带行为，通过跨语言分析发现词汇夹带具普遍性，声学韵律等方面有语境差异，分类器能检测夹带但优先考虑的特征与人类不同，引入评估模型决策框架并提出对话代理发展挑战。
AI 中文摘要
对话夹带在单语和书面语境中已得到充分研究，但在口语代码切换（CSW）中仍未得到充分探索。我们对普通话-英语、印地语-英语和西班牙语-英语对话中的夹带进行了新颖的跨语言分析，发现虽然词汇夹带在不同语言对中具有普遍性，但在声学韵律和CSW风格方面的夹带存在特定语境差异。通过询问分类模型是否捕捉到这些人类行为模式，我们发现经典和基于Transformer的分类器能较好地检测夹带，但始终优先考虑对人类夹带行为不太突出的特征。我们的方法引入了一个基于人类的框架来评估多语言文体语境中的模型决策，并为开发能够产生自然代码切换语音的对话代理提出了未来挑战。
英文摘要
Conversational entrainment is well-studied in monolingual and written contexts, but remains underexplored in spoken code-switching (CSW). We present a novel cross-lingual analysis of entrainment in Mandarin-English, Hindi-English, and Spanish-English dialogue and show that, while lexical entrainment generalizes across language pairs, entrainment over acoustic-prosodic and CSW style aspects exhibits context-specific variation. We build on these findings by asking whether classification models capture these human behavioral patterns. Applying feature importance and ablation analyses, we find that classical and Transformer-based classifiers detect entrainment reasonably well but consistently prioritize features other than those most salient to human entraining behavior. Our approach introduces a human-grounded framework for evaluating model decision-making in multilingual stylistic contexts, and suggests future challenges for developing conversational agents capable of producing naturalistic code-switched speech.
