RI-Medium: Collaborative: Corpus-Based Studies of Lexical, Acoustic-Prosodic, and Discourse Entrainment in Spoken Dialogue

RI-Medium：协作：基于语料库的口语对话中的词汇、声学韵律和话语夹带研究

• 批准号: 0803148
• 负责人: Julia Hirschberg
• 金额: $ 44.19万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0803148&HistoricalAwards=false
• 关键词: RI; Medium; Collaborative; Corpus; Based

Participants in human-human conversation often entrain to one another, adopting the vocabulary and other behaviors of their partners.Evidence of this has been found from laboratory studies and observations of real life situations. We are investigating many types of entrainment in two large corpora of human-human conversations to improve system behavior in Spoken Dialogue Systems (SDS). We want to discover which types of entrainment occur generally across speakers and which seem to be speaker-specific, which types of entrainment can be reliably linked to task success and perceived naturalness, and which types of entrainment can be automatically modeled in SDS.Our research has importance for the construction of better SDS.Currently, research SDS have attempted to entrain users to system vocabularies to improve speech recognition accuracy: Since users are likely to employ the same vocabulary in their answers that systems use in their queries, systems have a better chance of recognizing user input correctly if they can predict word usage. However, there has been little attempt to create SDS that entrain to user behavior, despite evidence that human beings rate humans and systems that behave more like them more highly than those that do not. Our work focuses on determining which types of system entrainment to users will be most important to users and most feasible for SDS. Our results will be disseminated through papers and presentations at speech and language conferences. We will also provide publicly available annotated corpora for future research by others.

人与人之间的对话的参与者经常互相吸引，采用对方的词汇和其他行为。实验室研究和对现实生活情况的观察已经发现了这一点的证据。 我们正在研究两个大型人与人对话语料库中的多种类型的夹带，以改善口语对话系统（SDS）中的系统行为。 我们想要发现哪些类型的夹带通常发生在说话者之间，哪些类型似乎是特定于说话者的，哪些类型的夹带可以与任务成功和感知的自然性可靠地联系起来，以及哪些类型的夹带可以在 SDS 中自动建模。我们的研究对于构建更好的 SDS 具有重要意义。目前，研究 SDS 试图引导用户使用系统词汇来提高语音识别的准确性：由于用户可能在答案中使用与系统在查询中使用的相同的词汇，因此系统有一个如果他们能够预测单词的使用，那么正确识别用户输入的机会就更大。 然而，尽管有证据表明人类对行为更像他们的人类和系统的评价高于那些行为不像他们的人类和系统，但几乎没有尝试创建引导用户行为的 SDS。 我们的工作重点是确定哪些类型的用户引导系统对用户来说最重要并且对于 SDS 来说最可行。 我们的研究结果将通过演讲和语言会议上的论文和演示来传播。 我们还将提供公开的带注释的语料库，供其他人未来的研究。