# Domain Adversarial for Acoustic Emotion Recognition

arXiv: https://arxiv.org/abs/1804.07690 · HTML: https://mlsanity.com/paper/1804.07690/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2018.

## Built on by (most cited first, 25 of 25)

- [LSSED: a large-scale dataset and benchmark for speech emotion recognition](https://mlsanity.com/paper/2102.01754/) (2021)
- [ADAPT : Awesome Domain Adaptation Python Toolbox](https://mlsanity.com/paper/2107.03049/) (2021)
- [Unsupervised Domain Adaptation Schemes for Building ASR in Low-resource Languages](https://mlsanity.com/paper/2109.05494/) (2021)
- [CTL-MTNet: A Novel CapsNet and Transfer Learning-Based Mixed Task Net for the Single-Corpus and Cross-Corpus Speech Emotion Recognition](https://mlsanity.com/paper/2207.10644/) (2022)
- [A cross-corpus study on speech emotion recognition](https://mlsanity.com/paper/2207.02104/) (2022)
- [Few-shot Learning in Emotion Recognition of Spontaneous Speech Using a Siamese Neural Network with Adaptive Sample Pair Formation](https://mlsanity.com/paper/2109.02915/) (2021)
- [A study on cross-corpus speech emotion recognition and data augmentation](https://mlsanity.com/paper/2201.03511/) (2022)
- [MSAC: Multiple Speech Attribute Control Method for Reliable Speech Emotion Recognition](https://mlsanity.com/paper/2308.04025/) (2023)
- [Unsupervised Domain Adaptation for Dysarthric Speech Detection via Domain Adversarial Training and Mutual Information Minimization](https://mlsanity.com/paper/2106.10127/) (2021)
- [Semi-supervised cross-lingual speech emotion recognition](https://mlsanity.com/paper/2207.06767/) (2022)
- [Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition](https://mlsanity.com/paper/2302.08921/) (2023)
- [CTA-RNN: Channel and Temporal-wise Attention RNN Leveraging Pre-trained ASR Embeddings for Speech Emotion Recognition](https://mlsanity.com/paper/2203.17023/) (2022)
- [Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extraction](https://mlsanity.com/paper/2204.11382/) (2022)
- [More Similar than Dissimilar: Modeling Annotators for Cross-Corpus Speech Emotion Recognition](https://mlsanity.com/paper/2509.12295/) (2025)
- [Acted vs. Improvised: Domain Adaptation for Elicitation Approaches in Audio-Visual Emotion Recognition](https://mlsanity.com/paper/2104.01978/) (2021)
- [Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unit](https://mlsanity.com/paper/2106.09539/) (2021)
- [Evaluating Deep Music Generation Methods Using Data Augmentation](https://mlsanity.com/paper/2201.00052/) (2021)
- [End-to-End Lip Reading in Romanian with Cross-Lingual Domain Adaptation and Lateral Inhibition](https://mlsanity.com/paper/2310.04858/) (2023)
- [DSNet: Disentangled Siamese Network with Neutral Calibration for Speech Emotion Recognition](https://mlsanity.com/paper/2312.15593/) (2023)
- [Mouth Articulation-Based Anchoring for Improved Cross-Corpus Speech Emotion Recognition](https://mlsanity.com/paper/2412.19909/) (2024)
- [Speaker Style-Aware Phoneme Anchoring for Improved Cross-Lingual Speech Emotion Recognition](https://mlsanity.com/paper/2509.20373/) (2025)
- [Emotion-Disentangled Embedding Alignment for Noise-Robust and Cross-Corpus Speech Emotion Recognition](https://mlsanity.com/paper/2510.09072/) (2025)
- [Robust Generative Audio Quality Assessment: Disentangling Quality from Spurious Correlations](https://mlsanity.com/paper/2603.16201/) (2026)
- [PASQA: Pitch-Accent-Focused Speech Quality Assessment Model Trained on Synthetic Speech with Accent Errors](https://mlsanity.com/paper/2606.20137/) (2026)
- [SISER: Speaker-Invariant Speech Emotion Recognition with Entropy-Based Adversarial Training](https://mlsanity.com/paper/2609.02941/) (2026)

## Abstract (arXiv)

The performance of speech emotion recognition is affected by the differences in data distributions between train (source domain) and test (target domain) sets used to build and evaluate the models. This is a common problem, as multiple studies have shown that the performance of emotional classifiers drop when they are exposed to data that does not match the distribution used to build the emotion classifiers. The difference in data distributions becomes very clear when the training and testing data come from different domains, causing a large performance gap between validation and testing performance. Due to the high cost of annotating new data and the abundance of unlabeled data, it is crucial to extract as much useful information as possible from the available unlabeled data. This study looks into the use of adversarial multitask training to extract a common representation between train and test domains. The primary task is to predict emotional attribute-based descriptors for arousal, valence, or dominance. The secondary task is to learn a common representation where the train and test domains cannot be distinguished. By using a gradient reversal layer, the gradients coming from the domain classifier are used to bring the source and target domain representations closer. We show that exploiting unlabeled data consistently leads to better emotion recognition performance across all emotional dimensions. We visualize the effect of adversarial training on the feature representation across the proposed deep learning architecture. The analysis shows that the data representations for the train and test domains converge as the data is passed to deeper layers of the network. We also evaluate the difference in performance when we use a shallow neural network versus a deep neural network (DNN) and the effect of the number of shared layers used by the task and domain classifiers.