Studies in classifying affect from vocal cues have produced exceptional within-corpus results, especially for arousal (activation or stress); yet cross-corpora affect recognition has only recently garnered attention. An essential requirement of many behavioral studies is affect scoring that generalizes across different social contexts and data conditions. We present a robust, unsupervised (rule-based) method for providing a scale-continuous, bounded arousal rating operating on the vocal signal. The method incorporates just three knowledge-inspired features chosen based on empirical and theoretical evidence. It constructs a speaker's baseline model for each feature separately, and then computes single-feature arousal scores. Lastly, it advantageously fuses the single-feature arousal scores into a final rating without knowledge of the true affect. The baseline data is preferably labeled as neutral, but some initial evidence is provided to suggest that no labeled data is required in certain cases. The proposed method is compared to a state-of-the-art supervised technique which employs a high-dimensional feature set. The proposed framework achieveshighly-competitive performance with additional benefits. The measure is interpretable, scale-continuous as opposed to discrete, and can operate without any affective labeling. An accompanying Matlab tool is made available with the paper.

中文翻译：

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稳健的无监督唤醒评级：具有知识启发声乐特征的基于规则的框架

从声音线索分类影响的研究产生了特殊的语料库内结果，特别是对于唤醒（激活或压力）；然而，跨语料库情感识别直到最近才引起关注。许多行为研究的一个基本要求是在不同的社会背景和数据条件下概括的影响评分。我们提出了一种鲁棒的、无监督的（基于规则的）方法，用于提供对声音信号进行操作的尺度连续、有界唤醒评级。该方法仅包含三个基于经验和理论证据选择的知识启发特征。它分别为每个特征构建说话者的基线模型，然后计算单特征唤醒分数。最后，它有利地将单特征唤醒分数融合到最终评级中，而无需了解真正的影响。基线数据最好被标记为中性，但提供了一些初始证据以表明在某些情况下不需要标记数据。将所提出的方法与采用高维特征集的最先进的监督技术进行比较。所提出的框架实现了极具竞争力的性能和额外的好处。该度量是可解释的、尺度连续的，而不是离散的，并且可以在没有任何情感标签的情况下运行。随附的 Matlab 工具随论文一起提供。将所提出的方法与采用高维特征集的最先进的监督技术进行比较。所提出的框架实现了极具竞争力的性能和额外的好处。该度量是可解释的、尺度连续的，而不是离散的，并且可以在没有任何情感标签的情况下运行。随附的 Matlab 工具随论文一起提供。将所提出的方法与采用高维特征集的最先进的监督技术进行比较。所提出的框架实现了极具竞争力的性能和额外的好处。该度量是可解释的、尺度连续的，而不是离散的，并且可以在没有任何情感标签的情况下运行。随附的 Matlab 工具随论文一起提供。