AI 中文总结
本研究提出多类交叉增强方案，消除噪声与类别标签关联，防止分类器利用噪声差异，在多类情感识别中有效缓解噪声影响，优于语音增强预处理。
AI 中文摘要
我们研究了多类语音分类任务中的噪声差异问题，并制定了一种策略，以防止分类器利用依赖于类别的噪声特征。基于我们先前在二分类方面的工作，我们引入了一种多类交叉增强方案，该方案使每个类别暴露于其他类别的噪声特征，从而消除个体噪声条件与类别标签之间的关联。我们将这种基于训练的方法与作为预处理策略的语音增强（SE）进行比较，后者旨在直接从输入中抑制与噪声相关的线索。在多类情感识别上的实验表明，交叉增强能有效缓解一系列信噪比下噪声差异的影响，而语音增强对模型性能产生不利影响。
英文摘要
We investigate noise disparity in multi-class speech classification tasks and develop a strategy to prevent classifiers from exploiting class-dependent noise characteristics. Building on our previous work for binary classification, we introduce a multi-class cross-augmentation scheme that exposes each class to the noise characteristics of the other classes, thereby removing the association between individual noise conditions and class labels. We compare this training-based approach with speech enhancement (SE) as a preprocessing strategy, which aims to suppress noise-related cues directly from the input. Experiments on multi-class emotion recognition show that cross-augmentation effectively mitigates the effect of noise disparity across a range of signal-to-noise-ratios, while SE has a detrimental effect on model performance.
