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首页> 外文期刊>International journal of entelligent systems >Context-Centric Speech-Based Human-Computer Interaction
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Context-Centric Speech-Based Human-Computer Interaction

机译：基于上下文的基于语音的人机交互

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This paper describes research that addresses the problem of dialog management from a strong, context-centric approach. We further present a quantitative method of measuring the importance of contextual cues when dealing with speech-based human-computer interactions. It is generally accepted that using context in conjunction with a human input, such as spoken speech, enhances a machine's understanding of the user's intent as a means to pinpoint an adequate reaction. For this work, however, we present a context-centric approach in which the use of context is the primary basis for understanding and not merely an auxiliary process. We employ an embodied conversation agent that facilitates the seamless engagement of a speech-based information-deployment entity by its human end user. This dialog manager emphasizes the use of context to drive its mixed-initiative discourse model. A typical, modern automatic speech recognizer (ASR) was incorporated to handle the speech-to-text translations. As is the nature of these ASR systems, the recognition rate is consistently less than perfect, thus emphasizing the need for contextual assistance. The dialog system was encapsulated into a speech-based embodied conversation agent platform for prototyping and testing purposes. Experiments were performed to evaluate the robustness of its performance, namely through measures of naturalness and usefulness, with respect to the emphasized use of context. The contribution of this work is to provide empirical evidence of the importance of conversational context in speech-based human-computer interaction using a field-tested context-centric dialog manager.
机译：本文介绍了一种以强大的，以上下文为中心的方法来解决对话框管理问题的研究。我们进一步提出了一种定量方法，用于处理基于语音的人机交互时上下文提示的重要性。通常认为，将上下文与诸如语音之类的人为输入结合使用，可以增强机器对用户意图的理解，以此作为确定适当反应的手段。但是，对于这项工作，我们提出了一种以上下文为中心的方法，其中使用上下文是理解的主要基础，而不仅仅是辅助过程。我们采用了一个具体化的对话代理，该代理促进了其人类最终用户对基于语音的信息部署实体的无缝参与。该对话管理器强调使用上下文来驱动其混合启动话语模型。结合了典型的现代自动语音识别器（ASR）来处理语音到文本的翻译。由于这些ASR系统的本质，识别率始终不尽人意，因此强调了对上下文帮助的需求。对话系统被封装到基于语音的会话代理平台中，以进行原型设计和测试。相对于强调使用上下文，进行了实验以评估其性能的鲁棒性，即通过自然性和有用性的度量。这项工作的贡献是，提供了使用现场测试的以上下文为中心的对话管理器在基于语音的人机交互中对话上下文的重要性的经验证据。

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