# Perceived Conversation Quality in Spontaneous Interactions

arXiv: https://arxiv.org/abs/2207.05791 · HTML: https://mlsanity.com/paper/2207.05791/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2022.

## Built on by (most cited first, 4 of 4)

- [End-to-End Label Uncertainty Modeling in Speech Emotion Recognition using Bayesian Neural Networks and Label Distribution Learning](https://mlsanity.com/paper/2209.15449/) (2022)
- [Multimodal Quantitative Measures for Multiparty Behaviour Evaluation](https://mlsanity.com/paper/2508.10916/) (2025)
- [REWIND Dataset: Privacy-preserving Speaking Status Segmentation from Multimodal Body Movement Signals in the Wild](https://mlsanity.com/paper/2403.01229/) (2024)
- [Augmenting Interviewer Judgments of Patient Experience with Automatic Language Analysis](https://mlsanity.com/paper/2608.31007/) (2026)

## Abstract (arXiv)

The quality of daily spontaneous conversations is of importance towards both our well-being as well as the development of interactive social agents. Prior research directly studying the quality of social conversations has operationalized it in narrow terms, associating greater quality to less small talk. Other works taking a broader perspective of interaction experience have indirectly studied quality through one of the several overlapping constructs such as rapport or engagement, in isolation. In this work we bridge this gap by proposing a holistic conceptualization of conversation quality, building upon the collaborative attributes of cooperative conversation floors. Taking a multilevel perspective of conversation, we develop and validate two instruments for perceived conversation quality (PCQ) at the individual and group levels. Specifically, we motivate capturing external raters' gestalt impressions of participant experiences from thin slices of behavior, and collect annotations of PCQ on the publicly available MatchNMingle dataset of in-the-wild mingling conversations. Finally, we present an analysis of behavioral features that are predictive of PCQ. We find that for the conversations in MatchNMingle, raters tend to associate smaller group sizes, equitable speaking turns with fewer interruptions, and time taken for synchronous bodily coordination with higher PCQ.