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

arXiv: https://arxiv.org/abs/2412.19909 · HTML: https://mlsanity.com/paper/2412.19909/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 27 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks](https://mlsanity.com/paper/1711.06753/) (2017)
- [Domain Adversarial for Acoustic Emotion Recognition](https://mlsanity.com/paper/1804.07690/) (2018)
- [Improving Cross-Corpus Speech Emotion Recognition with Adversarial Discriminative Domain Generalization (ADDoG)](https://mlsanity.com/paper/1903.12094/) (2019)
- [Semi-Supervised Speech Emotion Recognition with Ladder Networks](https://mlsanity.com/paper/1905.02921/) (2019)
- [A Layer-Anchoring Strategy for Enhancing Cross-Lingual Speech Emotion Recognition](https://mlsanity.com/paper/2407.04966/) (2024)

## Built on by (most cited first, 1 of 1)

- [Speaker Style-Aware Phoneme Anchoring for Improved Cross-Lingual Speech Emotion Recognition](https://mlsanity.com/paper/2509.20373/) (2025)

## Abstract (arXiv)

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.