A Cross-lingual Comparison of Human and Classification Model Entrainment Behavior in Code-switched Speech Settings

A new arXiv preprint (2607.25202v1) presents a cross-lingual analysis of conversational entrainment in Mandarin-English, Hindi-English, and Spanish-English code-switched speech, finding that lexical entrainment generalizes across language pairs but acoustic-prosodic and code-switching style entrainment varies by context. The study also shows that classical and Transformer-based classifiers detect entrainment reasonably well but prioritize different features than humans, highlighting challenges for developing naturalistic code-switched conversational agents.

arXiv:2607.25202v1 Announce Type: new Abstract: 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.