Mouth Articulation-Based Anchoring for Improved Cross-Corpus Speech Emotion Recognition

TL;DR

This paper introduces a novel cross-corpus speech emotion recognition method that emphasizes mouth articulatory gestures over traditional acoustic features, improving transferability and robustness across different datasets.

Contribution

It proposes a contrastive approach focusing on stable mouth articulatory gestures to enhance emotion transfer learning in SER tasks, addressing variability issues in acoustic features.

Findings

Mouth articulatory gestures are more consistent across datasets.

The method improves cross-corpus emotion recognition accuracy.

CREMA-D and MSP-IMPROV are effective benchmarks for evaluation.

Abstract

Cross-corpus speech emotion recognition (SER) plays a vital role in numerous practical applications. Traditional approaches to cross-corpus emotion transfer often concentrate on adapting acoustic features to align with different corpora, domains, or labels. However, acoustic features are inherently variable and error-prone due to factors like speaker differences, domain shifts, and recording conditions. To address these challenges, this study adopts a novel contrastive approach by focusing on emotion-specific articulatory gestures as the core elements for analysis. By shifting the emphasis on the more stable and consistent articulatory gestures, we aim to enhance emotion transfer learning in SER tasks. Our research leverages the CREMA-D and MSP-IMPROV corpora as benchmarks and it reveals valuable insights into the commonality and reliability of these articulatory gestures. The findings highlight mouth articulatory gesture potential as a better constraint for improving emotion recognition across different settings or domains.