域对抗用于声学情感识别
Domain Adversarial for Acoustic Emotion Recognition
AI总结：
本研究提出基于梯度反转层的对抗多任务训练方法，利用未标注数据对齐源域与目标域表示，以提升跨域语音情感识别性能，并验证了深层网络与共享层数量的影响。
摘要：
语音情感识别的性能受到用于构建和评估模型的数据分布差异的影响，即训练集（源域）和测试集（目标域）之间的数据分布差异。这是一个常见问题，因为多项研究表明，当情感分类器暴露于与构建情感分类器时所用分布不匹配的数据时，其性能会下降。当训练和测试数据来自不同领域时，数据分布的差异变得非常明显，导致验证性能和测试性能之间存在巨大差距。由于标注新数据成本高昂，且未标注数据丰富，因此从可用的未标注数据中提取尽可能多的有用信息至关重要。本研究探讨使用对抗性多任务训练来提取训练域和测试域之间的共同表示。主要任务是预测基于情感属性的描述符，即唤醒度、效价或支配度。次要任务是学习一个共同表示，使得训练域和测试域无法被区分。通过使用梯度反转层，来自域分类器的梯度被用于拉近源域和目标域的表示。我们表明，利用未标注数据始终能提高所有情感维度上的情感识别性能。我们可视化了对抗训练对所提出的深度学习架构中特征表示的影响。分析表明，随着数据传递到网络的更深层，训练域和测试域的数据表示逐渐收敛。我们还评估了使用浅层神经网络与深层神经网络（DNN）时的性能差异，以及任务和域分类器所使用的共享层数量的影响。
英文摘要：
The performance of speech emotion recognition is affected by the differences in data distributions between train (source domain) and test (target domain) sets used to build and evaluate the models. This is a common problem, as multiple studies have shown that the performance of emotional classifiers drop when they are exposed to data that does not match the distribution used to build the emotion classifiers. The difference in data distributions becomes very clear when the training and testing data come from different domains, causing a large performance gap between validation and testing performance. Due to the high cost of annotating new data and the abundance of unlabeled data, it is crucial to extract as much useful information as possible from the available unlabeled data. This study looks into the use of adversarial multitask training to extract a common representation between train and test domains. The primary task is to predict emotional attribute-based descriptors for arousal, valence, or dominance. The secondary task is to learn a common representation where the train and test domains cannot be distinguished. By using a gradient reversal layer, the gradients coming from the domain classifier are used to bring the source and target domain representations closer. We show that exploiting unlabeled data consistently leads to better emotion recognition performance across all emotional dimensions. We visualize the effect of adversarial training on the feature representation across the proposed deep learning architecture. The analysis shows that the data representations for the train and test domains converge as the data is passed to deeper layers of the network. We also evaluate the difference in performance when we use a shallow neural network versus a \emph{deep neural network} (DNN) and the effect of the number of shared layers used by the task and domain classifiers.
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。
