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.

在人与人之间的对话中，参与者经常相互牵连，采用对方的词汇和其他行为。这一点已经从实验室研究和对现实生活情景的观察中找到了证据。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(SD)中的系统行为。我们希望发现哪些类型的夹带一般发生在说话人之间，哪些类型似乎是特定于说话者的，哪些类型的夹带可以可靠地与任务成功和感知的自然度联系在一起，以及哪些类型的夹带可以在SD中自动建模。我们的研究对于构建更好的SD非常重要。目前，研究SD已经尝试将用户引入系统词汇以提高语音识别精度：由于用户可能在他们的答案中使用系统在他们的查询中使用的相同词汇，如果系统能够预测单词使用，系统有更好的机会正确识别用户输入。然而，很少有人尝试创建会影响用户行为的SD，尽管有证据表明，人类对行为更像他们的人和系统的评价比那些不像他们的人更高。我们的工作重点是确定哪些类型的系统对用户来说是最重要的，对用户来说是最可行的。我们的成果将通过在演讲和语言会议上发表的论文和演讲来传播。我们还将提供公开可用的带注释的语料库，以供其他人将来研究。