DIAC-WOZ数据集（二）---Visual signals
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视觉信号：
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Visual signals from the face-to-face data show that several features can serve as indicators of depression, anxiety, and PTSD (Scherer et al., 2013b; Scherer et al., 2014). Specifically, these forms of psychological distress are predicted by a more downward angle of the gaze, less intense smiles and shorter average durations of smile, as well as longer self-touches and fidget on average longer with both hands (e.g. rubbing, stroking) and legs (e.g. tapping, shaking). Moreover, the predictive ability of these indicators is moderated by gender (Stratou et al., 2013). A crossover interaction was observed between gender and distress level on emotional displays such as frowning,contempt,anddisgust. For example, men who scored positively for depression tend to display more frowning than men who did not, whereas women who scored positively for depression tend to display less frowning than those who did not. Other features such as variability of facial expressions show a main effect of gender – women tend to be more expressive than men, while still other observations, such as head-rotation variation, were entirely gender independent.
来自面对面数据的视觉信号表明，一些功能可以作为抑郁，焦虑和PTSD的指标（Scherer等，2013b； Scherer等，2014）。具体地说，这些心理困扰的形式通过一下指标来预测：凝视的向下角度更大，微笑强度降低和平均微笑持续时间较短，以及用双手（例如，抚摸，抚摸）和腿（例如轻拍，摇晃），更长的自我触摸和平均时间更长的手指。此外，这些指标的预测能力受性别影响（Stratou等，2013）。观察到性别与患病级别的一种运动显示之间的交叉互动，如皱着眉头，轻蔑和厌恶。例如，抑郁得分为正的男人比没有得分的男人更容易皱眉，而抑郁得分为正的女人比没有得分的男人更容易皱眉。面部表情的变异性等其他特征显示出性别的主要影响–女性往往比男性更具表现力，而其他诸如头部旋转度的观察结果则完全不依赖性别。
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非语言行为注解
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Severalnon-verbalbehaviors were annotated (Waxer, 1974; Hall et al., 1995): gaze directionality (up, down, left, right, towards interviewer), listening smiles (smiles while not speaking), selfadaptors (self-touches in the hand, body, and head), fidgetingbehaviors,andfoot-tappingorshakingbehaviors. Each behavior was annotated in a separate tier in ELAN. Four student annotators participated in the annotation; each tier was assigned to apair of annotators, who first went through a training phase until the inter-rater agreement (Krippendorff’salpha)exceeded0.7. Following training, each video was annotated by a single annotator; to monitor reliability, every 10–15 videos each pair was assigned the same video and inter-rater agreement was re-checked. Annotators were informed that their reliability was measured but did not know which videos were used for cross-checking (Wildman et al., 1975; Harris and Lahey, 1982).
注释了几种非语言行为（Waxer，1974； Hall等，1995）：注视方向（上，下，左，右，向面试官），倾听的微笑（不说话时微笑），自适应器（手中的自我触碰） ，身体和头部），获取行为以及踩踏或晃动行为。 每个行为都在ELAN的单独层中进行了注释。 四个学生注释者参加了注释； 每个等级都分配给一对注释者，他们首先经过培训阶段，直到评分者间协议（Krippendorff的alpha）超过0.7。 经过培训后，每个视频都由一个注释者来注释； 为了监控可靠性，对每10到15个视频分配了相同的视频，并且重新检查了评估者之间的协议。 注释者被告知他们的可靠性已经过测量，但是不知道哪些视频用于交叉检查（Wildman等，1975； Harris和Lahey，1982）。
In addition, automatic annotation of non-verbal features was carried out using a multimodal sensor fusion framework called MultiSense, with a multithreading architecture that enables different face- and body-tracking technologies to run in parallel and in realtime. Output from MultiSense was used to estimate the head orientation, the eye-gaze direction, smile level, and smile duration. Further, we automatically analyzed voice characteristics including speakers’ prosody(e.g.fundamentalfrequencyorvoiceintensity) and voice quality characteristics, on a breathy to tense dimension (Scherer et al., 2013a).
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此外，使用称为MultiSense的多模式传感器融合框架执行了非语言功能的自动注释，该框架具有多线程架构，该架构使不同的面部和身体跟踪技术能够并行并实时运行。 MultiSense的输出用于估计头部方向，视线方向，微笑水平和微笑持续时间。 此外，我们会在呼吸到紧张的维度上自动分析语音特征，包括说话者的韵律（例如基本频率或语音强度）和语音质量特征（Scherer等人，2013a）。
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