# Self Supervised Adversarial Domain Adaptation for Cross-Corpus and Cross-Language Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2204.08625 · HTML: https://mlsanity.com/paper/2204.08625/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2022.

## Built on by (most cited first, 13 of 13)

- [ExHuBERT: Enhancing HuBERT Through Block Extension and Fine-Tuning on 37 Emotion Datasets](https://mlsanity.com/paper/2406.10275/) (2024)
- [Emo-DNA: Emotion Decoupling and Alignment Learning for Cross-Corpus Speech Emotion Recognition](https://mlsanity.com/paper/2308.02190/) (2023)
- [MSAC: Multiple Speech Attribute Control Method for Reliable Speech Emotion Recognition](https://mlsanity.com/paper/2308.04025/) (2023)
- [Is It Still Fair? Investigating Gender Fairness in Cross-Corpus Speech Emotion Recognition](https://mlsanity.com/paper/2501.00995/) (2025)
- [Enhancing Speech Emotion Recognition Through Differentiable Architecture Search](https://mlsanity.com/paper/2305.14402/) (2023)
- [Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers](https://mlsanity.com/paper/2306.13804/) (2023)
- [Automatic Data Augmentation for Domain Adapted Fine-Tuning of Self-Supervised Speech Representations](https://mlsanity.com/paper/2306.00481/) (2023)
- [Integrating Contrastive Learning into a Multitask Transformer Model for Effective Domain Adaptation](https://mlsanity.com/paper/2310.04703/) (2023)
- [emoDARTS: Joint Optimisation of CNN & Sequential Neural Network Architectures for Superior Speech Emotion Recognition](https://mlsanity.com/paper/2403.14083/) (2024)
- [Uncertainty-aware Test-Time Training (UT3) for Efficient On-the-fly Domain Adaptive Dense Regression](https://mlsanity.com/paper/2509.03012/) (2025)
- [MI-Fuse: Label Fusion for Unsupervised Domain Adaptation with Closed-Source Large-Audio Language Model](https://mlsanity.com/paper/2509.20706/) (2025)
- [Improving Speech Emotion Recognition with Mutual Information Regularized Generative Model](https://mlsanity.com/paper/2510.10078/) (2025)
- [EM2LDL: A Multilingual Speech Corpus for Mixed Emotion Recognition through Label Distribution Learning](https://mlsanity.com/paper/2511.20106/) (2025)

## Abstract (arXiv)

Despite the recent advancement in speech emotion recognition (SER) within a single corpus setting, the performance of these SER systems degrades significantly for cross-corpus and cross-language scenarios. The key reason is the lack of generalisation in SER systems towards unseen conditions, which causes them to perform poorly in cross-corpus and cross-language settings. Recent studies focus on utilising adversarial methods to learn domain generalised representation for improving cross-corpus and cross-language SER to address this issue. However, many of these methods only focus on cross-corpus SER without addressing the cross-language SER performance degradation due to a larger domain gap between source and target language data. This contribution proposes an adversarial dual discriminator (ADDi) network that uses the three-players adversarial game to learn generalised representations without requiring any target data labels. We also introduce a self-supervised ADDi (sADDi) network that utilises self-supervised pre-training with unlabelled data. We propose synthetic data generation as a pretext task in sADDi, enabling the network to produce emotionally discriminative and domain invariant representations and providing complementary synthetic data to augment the system. The proposed model is rigorously evaluated using five publicly available datasets in three languages and compared with multiple studies on cross-corpus and cross-language SER. Experimental results demonstrate that the proposed model achieves improved performance compared to the state-of-the-art methods.