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arXiv · 2609.02941

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

Eunseo Choi·Hyunku Kang·Chanwoo Kim
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Abstract

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.

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Eunseo Choi, Hyunku Kang, Chanwoo Kim. 2026-08-31. SISER: Speaker-Invariant Speech Emotion Recognition with Entropy-Based Adversarial Training. https://arxiv.org/abs/2609.02941

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