Alayna Nguyen
Author directory2026
A Cross-lingual Comparison of Human and Classification Model Entrainment Behavior in Code-switched Speech Settings
Debasmita Bhattacharya | Siying Ding | Alayna Nguyen | Julia Hirschberg
Findings of the Association for Computational Linguistics: EMNLP 2026
Debasmita Bhattacharya | Siying Ding | Alayna Nguyen | Julia Hirschberg
Findings of the Association for Computational Linguistics: EMNLP 2026
Conversational entrainment is well-studied in monolingual and written contexts, but remains underexplored in spoken code-switching (CSW). We present a cross-lingual analysis of entrainment in Mandarin-English, Hindi-English, and Spanish-English dialogue, replicating and extending an established Spanish-English framework (Bhattacharya et al., 2024) to two new language pairs, 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. Using 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 challenges for developing conversational agents capable of producing naturalistic code-switched speech.
2024
Measuring Entrainment in Spontaneous Code-switched Speech
Debasmita Bhattacharya | Siying Ding | Alayna Nguyen | Julia Hirschberg
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Debasmita Bhattacharya | Siying Ding | Alayna Nguyen | Julia Hirschberg
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
It is well-known that speakers who entrain to one another have more successful conversations than those who do not. Previous research has shown that interlocutors entrain on linguistic features in both written and spoken monolingual domains. More recent work on code-switched communication has also shown preliminary evidence of entrainment on certain aspects of code-switching (CSW). However, such studies of entrainment in code-switched domains have been extremely few and restricted to human-machine textual interactions. Our work studies code-switched spontaneous speech between humans, finding that (1) patterns of written and spoken entrainment in monolingual settings largely generalize to code-switched settings, and (2) some patterns of entrainment on code-switching in dialogue agent-generated text generalize to spontaneous code-switched speech. Our findings give rise to important implications for the potentially “universal” nature of entrainment as a communication phenomenon, and potential applications in inclusive and interactive speech technology.