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.

论文介绍了一种名为 “NaturalTurn” 的对话分割算法，可将语音到文本转录本划分为对话转折点，从而更好地理解社交互动的基本构建模块。这一算法通过区分说话者的主要对话转折点和听者的次要话语，如背景话语、简短插话和其他特征对话的平行言论，来准确捕捉自然交流的动态。通过使用大量对话语料库的数据，研究者展示了通过 “NaturalTurn” 得到的转录本相较于现有方法的转录本具有更好的统计特性和推理特性。该算法代表着机器生成转录本处理方法或者所谓的 “转换模型” 的改进，将使研究人员能够将转换过程中的动态与社交互动的更广泛结果相关联，这是对话科学的一个核心目标。