Problem
Research questions and friction points this paper is trying to address.
Speech Emotion Recognition
labeled data scarcity
inter-speaker variability
SISER: Speaker-Invariant Speech Emotion Recognition with Entropy-Based Adversarial Training
🤖 AI Summary
本文提出SISER，通过整合wav2vec 2.0和ECAPA-TDNN于基于熵的对抗训练中，解决语音情感识别中的标注数据稀缺和说话人变异性问题。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
SISER
wav2vec 2.0
ECAPA-TDNN
entropy-based adversarial training
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