From Naturalness to Norms: Interactional Cultural Competence for SpeechLMs

Spoken language models (SpeechLMs) are increasingly real-time conversational actors. Yet many culturally consequential aspects of spoken interaction are not primarily lexical. Across sociolinguistics, linguistic anthropology, and conversation analysis, meaning emerges through how talk is produced and coordinated-prosody, timing, turn-taking, overlap, backchannels, and repair-within situated speech events. A transcript can be semantically correct yet interactionally inappropriate because many culture-bearing signals are audible and sequential rather than textual. This position paper argues for a speech-first view of cultural competence as interactional competence: the ability of a spoken agent to participate appropriately in event-situated interaction with locally normative conduct, while allowing plural acceptable realizations. Here, appropriate does not imply generic human-likeness; in many applications, the desired behavior may instead be constrained, neutral, predictable, or tool-like under an application-specific interaction contract. We synthesize social-science foundations into a theory-derived taxonomy of culture-bearing signals in speech, identify interactional phenomena where transcript correctness fails to predict appropriateness, and ground the agenda in today's SpeechLM stacks and evaluation practice. We propose an evaluation framing that complements WER/MOS and broad capability suites by making speech events and interaction contracts explicit, diagnosing where modern pipelines lose interactional cues, and treating cultural appropriateness as a norm-conditioned target rather than generic "naturalness.