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首页> 外文期刊>Affective Computing, IEEE Transactions on >Building Autonomous Sensitive Artificial Listeners
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Building Autonomous Sensitive Artificial Listeners

机译：建立自主的敏感人工听众

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This paper describes a substantial effort to build a real-time interactive multimodal dialogue system with a focus on emotional and nonverbal interaction capabilities. The work is motivated by the aim to provide technology with competences in perceiving and producing the emotional and nonverbal behaviors required to sustain a conversational dialogue. We present the Sensitive Artificial Listener (SAL) scenario as a setting which seems particularly suited for the study of emotional and nonverbal behavior since it requires only very limited verbal understanding on the part of the machine. This scenario allows us to concentrate on nonverbal capabilities without having to address at the same time the challenges of spoken language understanding, task modeling, etc. We first report on three prototype versions of the SAL scenario in which the behavior of the Sensitive Artificial Listener characters was determined by a human operator. These prototypes served the purpose of verifying the effectiveness of the SAL scenario and allowed us to collect data required for building system components for analyzing and synthesizing the respective behaviors. We then describe the fully autonomous integrated real-time system we created, which combines incremental analysis of user behavior, dialogue management, and synthesis of speaker and listener behavior of a SAL character displayed as a virtual agent. We discuss principles that should underlie the evaluation of SAL-type systems. Since the system is designed for modularity and reuse and since it is publicly available, the SAL system has potential as a joint research tool in the affective computing research community.
机译：本文介绍了构建实时互动多模式对话系统的大量工作，重点是情感和非语言交互功能。这项工作的目的是为技术提供感知和产生维持对话对话所需的情感和非语言行为能力。我们提出了一种敏感的人工听众（SAL）场景，该场景似乎特别适合于研究情感和非语言行为，因为它只需要机器部分非常有限的语言理解即可。此方案使我们能够专注于非语言能力，而不必同时解决口语理解，任务建模等方面的挑战。我们首先报告SAL方案的三个原型版本，在这些版本中，敏感的人工侦听器字符的行为由操作员确定。这些原型的目的是验证SAL方案的有效性，并允许我们收集构建系统组件所需的数据，以分析和综合相应的行为。然后，我们描述我们创建的完全自主的集成实时系统，该系统结合了用户行为的增量分析，对话管理以及显示为虚拟代理的SAL角色的说话人和听众行为的综合。我们讨论了应该作为SAL型系统评估基础的原则。由于该系统是为模块化和可重用而设计的，并且由于它是公开可用的，因此SAL系统具有作为情感计算研究社区中的联合研究工具的潜力。

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