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

2026-07-27

AI Summary

This study investigates differences between human interlocutors and classification models in their sensitivity to lexical, prosodic, and code-switching style cues during bilingual conversations, addressing a gap in cross-linguistic alignment research. Analyzing Mandarin–English, Hindi–English, and Spanish–English dyadic dialogues, the work combines feature importance analysis and ablation studies to compare how traditional classifiers and Transformer-based models respond to these multimodal signals. The research introduces the first cross-linguistic framework for modeling human alignment behavior and proposes a novel paradigm for evaluating multilingual dialogue systems against human behavioral benchmarks. Findings reveal that lexical alignment exhibits cross-linguistic universality, whereas prosodic and code-switching style alignment show language-pair specificity. Although models can detect alignment patterns, they rely on different feature cues than humans do.

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.

Problem

• conversational entrainment
• code-switching
• cross-lingual analysis
• multilingual speech
• human-model comparison

Innovation

• code-switching
• conversational entrainment
• cross-lingual analysis
• model interpretability
• multilingual speech