Comparing data-Driven and Handcrafted Features for Dimensional Emotion Recognition

Abstract

Speech Emotion Recognition (SER) has garnered significant attention over the past two decades. In the early stages of SER technology, ’brute force’-based techniques led to a significant expansion in knowledge-based acoustic feature representation (FR) for modeling sparse emotional data. However, as deep learning techniques have become more powerful, their direct application has been limited by the scarcity of well-annotated emotional data. As a result, pre-trained neural embeddings on large speech corpora have gained popularity for SER tasks. These embeddings leverage existing transfer learning methods suitable for general-purpose self-supervised learning (SSL) representations. Recent studies on downstream SSL techniques for dimensional SER have shown promising results. In this research, we aim to evaluate the emotion-discriminative characteristics of neural embeddings in general cases (out-of-domain) and when fine-tuned for SER (in-domain). Given that most SSL techniques are pre-trained primarily on English speech, we plan to use speech emotion corpora in both language-matched and mismatched conditions. We will assess the discriminative characteristics of both handcrafted and standalone neural embeddings as FRs.