论文标题
语音情感分类的情感概况炼油厂
Emotion Profile Refinery for Speech Emotion Classification
论文作者
论文摘要
人类的情绪本质上是模棱两可和不纯洁的。当设计系统以根据语音预测人类情绪时，必须考虑缺乏情感纯度。但是，大多数当前语音情感分类的方法都取决于共识，例如，一个硬性标签用于发言。考虑到情绪杂质，该标签原则对系统性能构成了挑战。在本文中，我们建议使用情感概况（EPS），该曲线提供了一个时间序列的段级软标签，以捕获特定语音话语中存在的情感线索的微妙融合。我们进一步提出了情感概况炼油厂（EPR），这是更新EPS的迭代程序。 EPR方法在连续的细化阶段产生软，动态生成的多个概率类标签，从而导致模型准确性的显着提高。对三个众所周知的情感语料库进行的实验显示了使用所提出的方法明显的增益。
Human emotions are inherently ambiguous and impure. When designing systems to anticipate human emotions based on speech, the lack of emotional purity must be considered. However, most of the current methods for speech emotion classification rest on the consensus, e.g., one single hard label for an utterance. This labeling principle imposes challenges for system performance considering emotional impurity. In this paper, we recommend the use of emotional profiles (EPs), which provides a time series of segment-level soft labels to capture the subtle blends of emotional cues present across a specific speech utterance. We further propose the emotion profile refinery (EPR), an iterative procedure to update EPs. The EPR method produces soft, dynamically-generated, multiple probabilistic class labels during successive stages of refinement, which results in significant improvements in the model accuracy. Experiments on three well-known emotion corpora show noticeable gain using the proposed method.
