  Conversation is the subject of increasing interest in the social, cognitive,
and computational sciences. And 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 introduce "NaturalTurn," a turn segmentation
algorithm designed to accurately capture the dynamics of naturalistic 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
conversation. Using data from a large conversation corpus, we show how
NaturalTurn-derived transcripts demonstrate favorable statistical and
inferential characteristics compared to transcripts derived from existing
methods. The NaturalTurn algorithm represents an improvement in
machine-generated transcript processing methods, or "turn models" that will
enable researchers to associate turn-taking dynamics with the broader outcomes
that result from social interaction, a central goal of conversation science.
