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

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.

Sound (cs.SD); Computation and Language (cs.CL)

arXiv:2609.02941 [cs.SD]

(or arXiv:2609.02941v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2609.02941 Focus to learn more arXiv-issued DOI via DataCite

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