SISER：基于熵对抗训练的说话人无关语音情感识别
SISER: Speaker-Invariant Speech Emotion Recognition with Entropy-Based Adversarial Training
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中文总结 AI 辅助
本文针对语音情感识别的标注数据稀缺与说话人差异性问题，提出基于熵对抗训练的SISER方法，采用wav2vec 2.0与ECAPA-TDNN，在IEMOCAP数据集上取得优于基线及无说话人抑制的wav2vec 2.0的性能。
中文摘要 AI 辅助
语音情感识别（SER）面临两大核心挑战：标注数据稀缺与说话人差异性，二者均会阻碍情感识别系统的泛化能力。现有对抗方法虽能处理说话人差异性，但未能充分利用强大的预训练表示。本文提出SISER（说话人无关语音情感识别），将wav2vec 2.0作为特征编码器、ECAPA-TDNN作为说话人判别器，整合至基于熵的对抗训练方案中。wav2vec 2.0提供丰富的自监督表示，缓解对大量标注数据集的依赖；ECAPA-TDNN相比浅层分类器，能通过更强的对抗信号抑制说话人身份。在IEMOCAP数据集上评估，SISER的未加权准确率（UA）达60.63%，优于基线（51.15%）和无说话人抑制的wav2vec 2.0（56.46%）， ablation实验表明，说话人分类器架构的选择是关键因素。
英文摘要
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.
