There is growing interest in affective computing for the representation and prediction of emotions along ordinal scales. However, the term ordinal emotion label has been used to refer to both absolute notions such as low or high arousal, as well as relation notions such as arousal is higher at one instance compared to another. In this paper, we introduce the terminology absolute and relative ordinal labels to make this distinction clear and investigate both with a view to integrate them and exploit their complementary nature. We propose a Markovian framework referred to as Dynamic Ordinal Markov Model (DOMM) that makes use of both absolute and relative ordinal information, to improve speech based ordinal emotion prediction. Finally, the proposed framework is validated on two speech corpora commonly used in affective computing, the RECOLA and the IEMOCAP databases, across a range of system configurations. The results consistently indicate that integrating relative ordinal information improves absolute ordinal emotion prediction.
翻译:人们越来越有兴趣用感官计算来表达和预测不同等级的情绪,然而,使用“常态情绪”标签来表示绝对概念,例如低或高振动,以及刺激等关系概念,在一个场合比另一个场合要高。在本文中,我们采用“绝对”和“相对”的术语标签,以明确这一区别,并调查两者,以期将两者结合起来,并利用其互补性。我们提议一个称为“动态奥迪纳尔·马尔科夫模型(DOMM)”的马尔科维安框架,它既利用绝对信息,也利用相对信息,以改进基于言语的情绪预测。最后,拟议框架在“有动性计算”中通常使用的两个语言组合(RECOLA)和“IEMOCAP”数据库(IEMOCAP)上得到验证,它跨越了各种系统配置。结果一致表明,将相对的常态信息整合可以改善绝对或非言调的情绪预测。