Gus Cooney

Andrew Reece, Gus Cooney, Peter Bull et al.

People spend a substantial portion of their lives engaged in conversation, and yet our scientific understanding of conversation is still in its infancy. In this report we advance an interdisciplinary science of conversation, with findings from a large, novel, multimodal corpus of 1,656 recorded conversations in spoken English. This 7+ million word, 850 hour corpus totals over 1TB of audio, video, and transcripts, with moment-to-moment measures of vocal, facial, and semantic expression, along with an extensive survey of speaker post conversation reflections. We leverage the considerable scope of the corpus to (1) extend key findings from the literature, such as the cooperativeness of human turn-taking; (2) define novel algorithmic procedures for the segmentation of speech into conversational turns; (3) apply machine learning insights across various textual, auditory, and visual features to analyze what makes conversations succeed or fail; and (4) explore how conversations are related to well-being across the lifespan. We also report (5) a comprehensive mixed-method report, based on quantitative analysis and qualitative review of each recording, that showcases how individuals from diverse backgrounds alter their communication patterns and find ways to connect. We conclude with a discussion of how this large-scale public dataset may offer new directions for future research, especially across disciplinary boundaries, as scholars from a variety of fields appear increasingly interested in the study of conversation.

Gus Cooney, Andrew Reece

Conversation is a subject of increasing interest in the social, cognitive, and computational sciences. Yet as conversational datasets continue to increase in size and complexity, researchers lack scalable methods to segment speech-to-text transcripts into conversational "turns"-the basic building blocks of social interaction. We discuss this challenge and then introduce "NaturalTurn," a turn-segmentation algorithm designed to accurately capture the dynamics of conversational exchange. NaturalTurn operates by distinguishing speakers' primary conversational turns from listeners' secondary utterances, such as backchannels, brief interjections, and other forms of parallel speech that characterize human conversation. Using data from a large conversation corpus, we show that NaturalTurn captures conversational turns more accurately than a baseline model. For example, it produces turns with durations and gaps that match empirical literature, reveals stronger linguistic alignment patterns between speakers, and uncovers otherwise hidden relationships between turn-taking and affective outcomes. NaturalTurn thus represents a pragmatic development in machine-generated transcript-processing methods, or "turn models", that will enable researchers to link turn-taking dynamics with important outcomes of social interaction, a central goal of conversation science.