Language conveys cultural identity even when not intentionally disclosed. This study examines cultural signals in task-oriented dialogue using English negotiations from the KODIS dataset. We focus our analysis on participants from four countries: the US, UK, Mexico, and South Korea. Interacting anonymously under identical conditions, we evaluated whether a speaker’s country could be inferred from dialogue by zero-shot LLMs and embedding-based classifiers. Results show that while objective negotiation outcomes remained similar across groups, subjective perceptions varied significantly. Embedding-based models reliably identified country of origin, whereas zero-shot LLM performance dropped under distribution shift. These findings suggest that cultural identity-related signals are embedded in language and may be relevant for analyzing negotiation dialogue.