Within a single speech emotion corpus, deep neural networks have shown decent performance in speech emotion recognition. However, the performance of the emotion recognition based on data-driven learning methods degrades significantly for the cross-corpus scenario. To relieve this issue without any labeled samples from the target domain, we propose a cross-corpus speech emotion recognition based on few-shot learning and unsupervised domain adaptation, which is trained to learn the class (emotion) similarity from the source domain samples adapted to the target domain. In addition, we utilize multiple corpora in training to enhance the robustness of the emotion recognition to the unseen samples. Experiments on emotional speech corpora with three different languages showed that the proposed method outperformed other approaches.

中文翻译：

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基于少样本学习和领域适应的跨语料库语音情感识别


在单个语音情感语料库中，深度神经网络在语音情感识别方面表现出了良好的性能。然而，基于数据驱动学习方法的情感识别的性能在跨语料库场景下显着下降。为了在没有来自目标域的任何标记样本的情况下解决这个问题，我们提出了一种基于少样本学习和无监督域适应的跨语料库语音情感识别，它被训练以从适应的源域样本中学习类（情感）相似性到目标域。此外，我们在训练中利用多个语料库来增强对未见过的样本的情感识别的鲁棒性。对三种不同语言的情感语音语料库的实验表明，所提出的方法优于其他方法。