Guiding Spoken Dialogue with Computers by Responding to Prosodic Cues

通过响应韵律提示来指导与计算机的口语对话

• 批准号: 9505156
• 负责人: David Mostow
• 金额: $ 42.09万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-05-15 至 1999-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9505156&HistoricalAwards=false
• 关键词: Guiding; Spoken; Dialogue; Computers; Responding

To carry on effective, natural, spoken dialogue,computers will need to do more than recognize and interpret spoken words -- they will also need to be sensitive to the prosodic information encoded in how the words are spoken. To investigate the hypothesis that detecting and exploiting prosodic cues can help computers guide spoken dialogue, this case study focuses on an educational task that combines intrinsic national importance with compelling methodological advantages: listening to a child read aloud, and providing spoken assistance. The research focusses on improving four aspects of dialogue -- taking turns, handling speech repairs, preventing dialogue breakdown, and modelling the speaker. In the reading task, these aspects include detecting a number of pedagogically significant events, such as when readers complete a passage, correct themselves, or encounter difficulty in identifying a word or comprehending a passage. The goal is to use prosodic cues to help detect these events in order to make the dialogue between student and computer more effective in achieving its educational objectives . Expected outcomes include improvements in the automated reading assistant, the discovery of robust prosodic phenomena, methods for detecting them, and principles for using them to achieve graceful, effective spoken dialogue between humans and computers.

为了进行有效、自然的口语对话，计算机需要做的不仅仅是识别和解释口语单词--它们还需要对单词发音中编码的韵律信息敏感。 要调查的假设，检测和利用韵律线索可以帮助计算机指导口语对话，本案例研究的重点是教育任务，结合了内在的国家的重要性，令人信服的方法优势：听孩子大声朗读，并提供口头援助。 该研究的重点是改善四个方面的对话-轮流，处理语音修复，防止对话故障，并模拟扬声器。 在阅读任务中，这些方面包括检测许多教学上重要的事件，例如当读者完成一篇文章时，纠正自己，或者在识别单词或理解文章时遇到困难。 我们的目标是使用韵律线索来帮助检测这些事件，以使学生和计算机之间的对话更有效地实现其教育目标。 预期成果包括改进自动化阅读助手，发现强大的韵律现象，检测它们的方法，以及使用它们来实现人类和计算机之间优雅，有效的口语对话的原则。