自然转:一种将语音分割成心理上有意义的对话转折的方法
Gus Cooney1,2, Andrew Reece3
1The Wharton School, University of Pennsylvania, Philadelphia, PA, USA. guscooney@gmail.com.
Scientific reports
|November 7, 2025
概括
研究人员开发了NaturalTurn,这是一个新的算法,用于将对话语音到文本转录细分为转换. 这种方法准确地识别了主讲者转向与次要听众发言,改善了对社交互动动态的分析.
科学领域:
- 计算语言学 计算语言学
- 社会科学 社会科学 社会科学
- 认知科学 认知科学
背景情况:
- 分析对话数据对于社会,认知和计算科学至关重要.
- 现有的方法难以准确地将语音转录成对话转换,这阻碍了研究.
- 需要可扩展的方法来处理大型,复杂的对话数据集.
研究的目的:
- 介绍NaturalTurn,一个用于准确对话转折细分的算法.
- 为了区分主扬声器转向与次要听众发言 (例如,后台通道).
- 为了能够更深入地分析对话动态及其与社会结果的关系.
主要方法:
- 开发了NaturalTurn算法来区分初级和二级对话发言.
- 将NaturalTurn应用到一个大型对话库中.
- 将NaturalTurn的性能与基线细分模型进行比较.
主要成果:
- 与基线相比,NaturalTurn在捕捉对话转方面表现出更高的准确性.
- 该算法产生了与经验文献一致的转折持续时间和差距.
- "自然转换"揭示了更强的语言对齐,并揭示了转换和情感结果之间的隐藏联系.
结论:
- 在处理对话成绩单方面,NaturalTurn提供了务实的进步.
- 这种方法有助于研究将轮流动力学与重要的社会互动结果联系起来.
- 使研究人员能够更好地了解人类对话的结构和影响.
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