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首页> 外文期刊>Journal on multimodal user interfaces >A generic framework for the inference of user states in human computer interaction How patterns of low level behavioral cues support complex user states in HCI
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A generic framework for the inference of user states in human computer interaction How patterns of low level behavioral cues support complex user states in HCI

机译：人机交互中推断用户状态的通用框架低级别行为提示的模式如何支持HCI中的复杂用户状态

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The analysis of affective or communicational states in human-human and human-computer interaction (HCI) using automatic machine analysis and learning approaches often suffers from the simplicity of the approaches or that very ambitious steps are often tried to be taken at once. In this paper, we propose a generic framework that overcomes many difficulties associated with real world user behavior analysis (i.e. uncertainty about the ground truth of the current state, subject independence, dynamic realtime analysis of multimodal information, and the processing of incomplete or erroneous inputs, e.g. after sensor failure or lack of input). We motivate the approach, that is based on the analysis and spotting of behavioral cues that are regarded as basic building blocks forming user state specific behavior, with the help of related work and the analysis of a large HCI corpus. For this corpus paralinguistic and nonverbal behavior could be significantly associated with user states. Some of our previous work on the detection and classification of behavioral cues is presented and a layered architecture based on hidden Markov models is introduced. We believe that this step by step approach towards the understanding of human behavior underlined by encouraging preliminary results outlines a principled approach towards the development and evaluation of computational mechanisms for the analysis of multimodal social signals.
机译：使用自动机器分析和学习方法对人与人和人机交互（HCI）中的情感或交流状态进行分析通常会遭受方法简单性的困扰，或者往往试图立即采取非常雄心勃勃的步骤。在本文中，我们提出了一个通用框架，该框架克服了与现实世界用户行为分析相关的许多困难（即，关于当前状态的基本事实的不确定性，主题独立性，多模式信息的动态实时分析以及对不完整或错误输入的处理） （例如，传感器故障或缺少输入后）。我们在相关工作和对大型HCI语料库的帮助下，基于对行为线索的分析和发现（这些行为线索被视为形成特定于用户状态的行为的基本构建块）来激励这种方法。对于此语料库，副语言和非语言行为可能与用户状态显着相关。介绍了我们先前对行为线索的检测和分类的一些工作，并介绍了基于隐马尔可夫模型的分层体系结构。我们认为，通过鼓励初步结果强调的逐步理解人类行为的方法，概述了开发和评估用于分析多模式社会信号的计算机制的原则方法。

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