Turn-taking is a fundamental component of human communication and is signalled through complex cues distributed across lexical, temporal, and prosodic information. Full-duplex models of spoken dialogue integrate these information sources to produce impressive turn-taking behaviour. Yet, existing evaluations of their turn-taking capabilities do not address which information sources drive predictions.We present a systematic analysis of the role of lexical-temporal features on the predictability of turn structure by examining PairwiseTurnGPT, a full-duplex model of spoken dialogue transcripts. Through PCA, mixed-effects modelling, and temporal surprisal analysis, we reveal context-dependent patterns: linguistic fluency paradoxically creates overconfidence at intermediate completion points, while turn-shift overlap dominates boundary detection. Our findings uncover where lexical-temporal information suffices and where additional cues become necessary, establishing a deeper understanding of how turn-taking cues are distributed and how to evaluate dialogue systems.

We present information value, a measure which quantifies the predictability of an utterance relative to a set of plausible alternatives. We introduce a method to obtain interpretable estimates of information value using neural text generators, and exploit their psychometric predictive power to investigate the dimensions of predictability that drive human comprehension behaviour. Information value is a stronger predictor of utterance acceptability in written and spoken dialogue than aggregates of token-level surprisal and it is complementary to surprisal for predicting eye-tracked reading times.

There has been a surge of interest regarding the alignment of large-scale language models with human language comprehension behaviour. The majority of this research investigates comprehension behaviours from reading isolated, written sentences. We propose studying the perception of dialogue, focusing on an intrinsic form of language use: spoken conversations. Using the task of predicting upcoming dialogue turns, we ask whether turn plausibility scores produced by state-of-the-art language models correlate with human judgements. We find a strong correlation for some but not all models: masked language models produce stronger correlations than auto-regressive models. In doing so, we quantify human performance on the response selection task for open-domain spoken conversation. To the best of our knowledge, this is the first such quantification. We find that response selection performance can be used as a coarse proxy for the strength of correlation with human judgements, however humans and models make different response selection mistakes. The model which produces the strongest correlation also outperforms human response selection performance. Through ablation studies, we show that pre-trained language models provide a useful basis for turn representations; however, fine-grained contextualisation, inclusion of dialogue structure information, and fine-tuning towards response selection all boost response selection accuracy by over 30 absolute points.