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

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.

Computation and Language (cs.CL)

arXiv:2607.25202 [cs.CL]

(or arXiv:2607.25202v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.25202 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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