Conversations in collaborative settings involve evolving social and cognitive dynamics such as coordination, cognitive load, and participation asymmetry, which are not directly observable but manifest through interactional behavior over time. Prior work has largely relied on static summaries or post-hoc labels, limiting our ability to capture how these dynamics unfold during interaction. We model dyadic conversation as a partially observable temporal process and propose a weakly supervised framework for tracking latent conversational state from interaction and acoustic behavioral signals. Using a dataset of remote dyadic conversations (53 dyads) over 9 collaborative tasks with task-level annotations, we segment interactions into fixed temporal windows of 30 seconds and extract 34 features capturing interactional features (turn-taking, floor control) and speaker-relative acoustic measures. We compare static models (Ridge, Random Forest), sequential neural models (GRU with pooling and attention), and two strategies for temporal trajectory modeling of latent states. Static interaction features remain the strongest predictor for both temporal demand (correlation = 0.254) and mental demand (correlation = 0.217); but we do not claim temporal modeling improves prediction accuracy over static baselines. More importantly, temporal modeling reveals interpretable latent trajectory structures – escalation, convergence, and participation imbalance, which are not observable in aggregated summary features alone and can vary systematically by task type and cognitive demand level. These findings provide a step toward dynamic, interpretable representations of conversational state in human conversations.

Despite significant advances in automatic speech recognition (ASR) accuracy, challenges remain. Naturally occurring conversation often involves multiple overlapping speakers, of different ages, accents and genders, as well as noisy environments and suboptimal audio recording equipment, all of which reduce ASR accuracy. In this study, we evaluate the accuracy of state of the art open source ASR systems across diverse conversational speech datasets, examining the impact of audio and speaker characteristics on WER. We then explore the potential of ASR ensembling and post-ASR correction methods to improve transcription accuracy. Our findings emphasize the need for robust error correction techniques and of continuing to address demographic biases to enhance ASR performance and inclusivity.