RI: Small: Flexible Turn-Taking for Mixed-Initiative Spoken Dialogue System

RI：小：混合主动口语对话系统的灵活轮流

• 批准号: 1321146
• 负责人: Peter Heeman
• 金额: $ 25万
• 依托单位: Oregon Health & Science University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1321146&HistoricalAwards=false
• 关键词: RI; Small; Flexible; Turn; Taking

The goal of this project is to determine how turn-taking in human-human dialogue works, and use this as a basis for developing a flexible model of turn-taking for use in spoken dialogue systems. The first two aims focus on determining whether turn-taking is solely determined by the current speaker or is negotiated by both conversants. The first aim measures how well human subjects, listening to excerpts of speech, can predict whether the current speaker will continue the turn or release it. The second aim analyzes timing lags at turn transitions. Many turn transitions have short lags, which favors a speaker-control model; however this might be an artifact of common speech act sequences, back channels, early onsets, and provisional turns. The third aim, using the findings of the first two aims, is to determine the cues and mechanisms that are used by conversants in taking the turn.Better understanding of turn-taking in human-human dialogues is important as it will help in building more efficient and natural spoken dialogue systems, allowing the user and system to better collaborate to solve complex tasks. It should also allow spoken dialogue systems to deal with a broad range of users, from experts to novices: for experts, letting them take the initiative (and the turn), in order to efficiently complete the task, while guiding novices with more directions and examples. Furthermore, understanding human-human turn-taking might have biomedical applications. For example, Autism, which is a disorder that affects social communication, might impact how people engage in turn-taking, and so turn-taking biomarkers might help in diagnosing it.

该项目的目标是确定人与人对话中轮流的工作原理，并以此为基础开发用于口语对话系统的灵活轮流模型。前两个目标侧重于确定轮流是否完全由当前发言者决定，还是由双方协商确定。第一个目标是测量人类受试者在听语音摘录时预测当前说话者是否会继续转弯或释放转弯的能力。第二个目标分析转弯转换时的时间滞后。许多转弯转换都有较短的滞后，这有利于扬声器控制模型；然而，这可能是常见言语行为序列、反向通道、早期发作和临时转向的产物。第三个目标是利用前两个目标的发现，确定对话者在轮流中使用的线索和机制。更好地理解人与人对话中的轮流非常重要，因为它将有助于构建更高效、更自然的口语对话系统，使用户和系统能够更好地协作来解决复杂的任务。它还应该让口语对话系统能够应对广泛的用户，从专家到新手：对于专家来说，让他们采取主动（和轮流），以便高效地完成任务，同时用更多的方向和例子来指导新手。此外，理解人与人之间的轮流可能具有生物医学应用。例如，自闭症是一种影响社交沟通的疾病，可能会影响人们进行轮流的方式，因此轮流生物标志物可能有助于诊断它。