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Modelling the effects of constraint upon speech-based human-computer interaction

机译：建模约束对基于语音的人机交互的影响

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摘要

Commercial speech systems, for use by the public, rely heavily on prompts which aim to constrain user input to a highly limited vocabulary set. Constraints help to increase the recognition accuracy of the automatic speech recognition device and thus improve dialogue efficiency. However, this strategy can also lengthen interactions because longer prompts are needed to effectively constrain user utterances and more steps are usually needed to complete a task. The current paper argues that to achieve optimal dialogue design solutions it is necessary to balance these conflicting effects of constraint. Two modelling studies are presented in which dialogue constraint levels for a home-banking application were systematically manipulated in order to investigate the effects on overall transaction time. The results indicate that, even with the assumption that high constraint leads to high recognition accuracy, it is difficult for highly constrained dialogues which entail the need for extra dialogue steps (e.g. those using menus) to compete with less-constrained dialogues which do not (e.g. those using queries). The implications of these findings to system design are discussed and it is suggested that the modelling method presented here can provide a useful tool early in the design process.
机译：供公众使用的商业语音系统在很大程度上依赖于提示，这些提示旨在将用户输入限制为一个非常有限的词汇集。约束有助于提高自动语音识别设备的识别精度，从而提高对话效率。但是，此策略还可以延长交互时间，因为需要更长的提示来有效地限制用户的话语，并且通常需要更多的步骤来完成任务。当前的论文认为，要获得最佳的对话设计解决方案，必须平衡约束的这些冲突影响。提出了两个建模研究，其中系统地操纵了家庭银行应用程序的对话约束级别，以调查对总体交易时间的影响。结果表明，即使假设高约束条件会导致较高的识别准确度，对于高度受限的对话还是很困难的，这需要额外的对话步骤（例如，使用菜单的对话）才能与不具有约束力的较低对话进行竞争（例如使用查询的对象）。讨论了这些发现对系统设计的影响，并建议此处介绍的建模方法可以在设计过程的早期提供有用的工具。

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