# SISER: Speaker-Invariant Speech Emotion Recognition with Entropy-Based Adversarial Training

arXiv: https://arxiv.org/abs/2609.02941 · HTML: https://mlsanity.com/paper/2609.02941/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 31 Aug 2026.

## Builds on (most cited first, 16 of 16)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation](https://mlsanity.com/paper/2312.15185/) (2023)
- [Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings](https://mlsanity.com/paper/2104.03502/) (2021)
- [Attentive Statistics Pooling for Deep Speaker Embedding](https://mlsanity.com/paper/1803.10963/) (2018)
- [Speech Emotion Recognition using Self-Supervised Features](https://mlsanity.com/paper/2202.03896/) (2022)
- [Domain Adversarial for Acoustic Emotion Recognition](https://mlsanity.com/paper/1804.07690/) (2018)
- [Speaker-Invariant Training via Adversarial Learning](https://mlsanity.com/paper/1804.00732/) (2018)
- [Self-supervised Fine-tuning for Improved Content Representations by Speaker-invariant Clustering](https://mlsanity.com/paper/2305.11072/) (2023)
- [Speaker-invariant Affective Representation Learning via Adversarial Training](https://mlsanity.com/paper/1911.01533/) (2019)
- [Deep neural networks for emotion recognition combining audio and transcripts](https://mlsanity.com/paper/1911.00432/) (2019)
- [R-Spin: Efficient Speaker and Noise-invariant Representation Learning with Acoustic Pieces](https://mlsanity.com/paper/2311.09117/) (2023)
- [Towards adversarial learning of speaker-invariant representation for speech emotion recognition](https://mlsanity.com/paper/1903.09606/) (2019)
- [Beyond Short Segments : Expanding Speaker Embeddings with Vector Archives](https://mlsanity.com/paper/2609.25007/) (2026)

## Abstract (arXiv)

Speech emotion recognition (SER) faces two fundamental challenges: scarcity of labeled data and inter-speaker variability, both of which hinder generalization of emotion recognition systems. While prior adversarial approaches address speaker variability, they fall short in leveraging powerful pre-trained representations. We propose SISER (Speaker-Invariant Speech Emotion Recognition), integrating wav2vec 2.0 as a feature encoder and ECAPA-TDNN as a speaker discriminator within an entropy-based adversarial training scheme. wav2vec 2.0 provides rich self-supervised representations that alleviate dependency on large labeled datasets, while ECAPA-TDNN enables suppression of speaker identity via a stronger adversarial signal than shallow classifiers. Evaluated on IEMOCAP, SISER achieves a UA of 60.63%, outperforming the baseline (51.15%) and wav2vec 2.0 without speaker suppression (56.46%), with ablation emphasizing that the choice of speaker classifier architecture is a key factor.