RI-Medium: Collaborative Research : Corpus-based Studies of Lexical, Acoustic, And Discourse Entrainment in Spoken Dialogue

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

• 批准号: 0803159
• 负责人: Ani Nenkova
• 金额: $ 34.38万
• 依托单位: University of Pennsylvania
• 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 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0803159&HistoricalAwards=false
• 关键词: RI; Medium; Collaborative; Research; Corpus

Participants in human-human conversation often entrain to one another, adopting thevocabulary and other behaviors of their partners.Evidence of this has been found from laboratory studies and observations of real lifesituations. We are investigating many types of entrainment in two large corpora ofhuman-human conversations to improve system behavior in Spoken Dialogue Systems(SDS). We want to discover which types of entrainment occur generally acrossspeakers and which seem to be speaker-specific, which types of entrainment can bereliably linked to task success and perceived naturalness, and which types ofentrainment 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 toimprove speech recognition accuracy: Since users are likely to employ the samevocabulary in their answers that systems use in their queries, systems have a betterchance 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, despiteevidence that human beings rate humans and systems that behave more like themmore highly than those that do not. Our work focuses on determining which types ofsystem 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 andlanguage conferences. We will also provide publicly available annotated corpora forfuture research by others.

人类对话的参与者经常互相融合，采用vocabulary和其他伴侣的其他行为。我们正在调查两个人类人类对话的两个大型语料库中的许多类型的夹带，以改善口语对话系统（SDS）中的系统行为。我们想发现哪种类型的夹杂物通常发生在跨言言扬声器，哪些类型的夹带可以与任务成功和自然性相关，以及哪些类型的entrainment可以自动建立在SDS中。 Same Vocabulary在他们的答案中，系统在查询中使用，系统可以更好地识别用户输入，如果他们可以预测单词用法。但是，尽管人们对人类和系统的行为比那些不这样做的人的行为更大，但几乎没有尝试创建夹其中用户行为的SD。我们的工作重点是确定对用户最重要的系统夹带，对于用户最重要，对于SDS，我们的结果将通过语音和语言会议上的论文和演示来传播。我们还将提供其他人的公开注释的Corpora Future研究。