Robust Unsupervised Arousal Rating: A Rule-Based Framework with Knowledge-Inspired Vocal Features
Robust Unsupervised Arousal Rating: A Rule-Based Framework with Knowledge-Inspired Vocal Features
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DOI：
10.1109/taffc.2014.2326393
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发表时间：
2014-04-01
影响因子：
11.2
通讯作者：
Narayanan, Shrikanth
中科院分区：
文献类型：
--
作者：
Bone, Daniel;Lee, Chi-Chun;Narayanan, Shrikanth
从声音线索分类影响的研究产生了特殊的语料库内的结果，特别是唤醒（激活或压力）;然而，跨语料库的影响识别最近才获得关注。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进行情感评分。我们提出了一个强大的，无监督（基于规则）的方法，提供一个规模连续的，有界的唤醒评级上的声音信号。该方法仅结合了三个基于经验和理论证据选择的知识启发特征。该方法分别为每个特征构建说话人的基线模型，然后计算单个特征的唤醒分数。最后，它有利地将单特征唤醒分数融合到最终评级中，而不知道真实的影响。基线数据最好标记为中性，但提供一些初步证据表明在某些情况下不需要标记数据。所提出的方法相比，一个国家的最先进的监督技术，采用了高维特征集。所提出的框架实现了高竞争力的性能与额外的好处。该措施是可解释的，规模连续的，而不是离散的，并可以在没有任何情感标签的情况下操作。一个附带的Matlab工具是提供与文件。
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 achieves highly-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.