SISER: Speaker-Invariant Speech Emotion Recognition with Entropy-Based Adversarial Training
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 v...
| Authors | Eunseo Choi, Hyunku Kang, Chanwoo Kim |
|---|---|
| Primary category | cs.SD |
| Published | 31 August 2026 |
| Last updated at source | 31 August 2026 |
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