CAREER: Prevention, Detection and Repair of Miscommunication in Spoken Natural Language Dialog Processing Systems

语音识别技术的最新发展使自然语言对话成为人机交互的一种可行手段。 一个尚未解决的重要问题是如何处理沟通不畅。 通过研究先前记录的人-人和人-计算机对话，将研究以下策略以减少自然语言对话中的误解：（1）扩展用于输入理解的期望驱动模型;（2）开发用于选择性验证用户输入的基于上下文的模型;以及（3）开发用于参与子对话以解决误解的方法。 这些理论将在模拟和实验条件下实施和评估。 该项目的结果将为评估当前对话理论的充分性提供一个新的基准，并提供有关口语人机对话特征的进一步数据。 该项目还将制定几个丰富的教育方案，以支持北卡罗来纳州主要是农村和经济欠发达的东部地区。

Recent improvements in speech recognition technology has made spoken natural language dialog a viable means of human-computer interaction. An important unresolved issue is the handling of miscommunication. By studying previously recorded human-human and human-computer dialogs, the following strategies for reducing miscommunication in natural language dialog will be investigated: ( 1) extending an expectation-driven model for input understanding; (2) developing a context-based model for selective verification of user inputs; and (3) developing methods for engaging in subdialogs for resolving miscommunications. These theories will be implemented and evaluated under both simulated and experimental conditions. The results from this project will provide a new benchmark in evaluating the adequacy of current dialog theory as well as provide further data on the characteristics of spoken human-machine dialog. The project will also develop several enrichment educational programs in support of the predominantly rural and less economically developed eastern part of North Carolina.