A Novel Markovian Framework for Integrating Absolute and Relative Ordinal Emotion Information

TL;DR

This paper introduces a Markovian framework that combines absolute and relative ordinal emotion labels to enhance speech-based emotion prediction, validated on two speech corpora showing improved accuracy.

Contribution

The paper proposes the Dynamic Ordinal Markov Model (DOMM), a novel framework that integrates absolute and relative ordinal emotion information for better prediction.

Findings

Integrating relative ordinal information improves prediction accuracy.

Validated on RECOLA and IEMOCAP databases.

Consistent performance gains across system configurations.

Abstract

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.