面向噪声鲁棒和跨语料库语音情感识别的情感解耦嵌入对齐
Emotion-Disentangled Embedding Alignment for Noise-Robust and Cross-Corpus Speech Emotion Recognition
AI总结：
本文提出EDRL-MEA两步方法，通过情感解耦表征学习提取判别特征并利用多块嵌入对齐投影到联合子空间，显著提升了语音情感识别在噪声和跨语料库场景下的鲁棒性与泛化能力。
摘要：
语音情感识别在真实场景中的有效性常受噪声环境和数据集间差异的阻碍。本文提出一种两步方法，通过改进表征学习来增强语音情感识别模型的鲁棒性和泛化能力。首先，模型采用EDRL（情感解耦表征学习）提取类特定判别特征，同时保留跨情感类别的共享相似性。接着，MEA（多块嵌入对齐）通过将这些表征投影到与原始语音输入最大化协方差的联合判别潜在子空间来精炼它们。学习到的EDRL-MEA嵌入随后用于使用公开数据集的干净样本训练情感分类器，并在未见过的噪声和跨语料库语音样本上进行评估。在这些挑战性条件下性能的提升证明了所提方法的有效性。
英文摘要：
Effectiveness of speech emotion recognition in real-world scenarios is often hindered by noisy environments and variability across datasets. This paper introduces a two-step approach to enhance the robustness and generalization of speech emotion recognition models through improved representation learning. First, our model employs EDRL (Emotion-Disentangled Representation Learning) to extract class-specific discriminative features while preserving shared similarities across emotion categories. Next, MEA (Multiblock Embedding Alignment) refines these representations by projecting them into a joint discriminative latent subspace that maximizes covariance with the original speech input. The learned EDRL-MEA embeddings are subsequently used to train an emotion classifier using clean samples from publicly available datasets, and are evaluated on unseen noisy and cross-corpus speech samples. Improved performance under these challenging conditions demonstrates the effectiveness of the proposed method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
