A Novel Markovian Framework for Integrating Absolute and Relative Ordinal Emotion Information
This work addresses the challenge of better emotion modeling in affective computing, though it is incremental as it builds on existing ordinal frameworks.
The paper tackles the problem of improving ordinal emotion prediction from speech by integrating absolute and relative ordinal labels, showing that incorporating relative information consistently enhances prediction accuracy on the RECOLA and IEMOCAP databases.
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.