Speech emotion recognition has been developed rapidly in recent decades because of the appearance of machine learning. Nevertheless, lack of corpus remains a significant issue. For actual speech emotion corpus construction, many professional actors are required to perform voices with various emotions in specific scenes. In the process of data labelling, since the number of samples of different emotion categories is extremely imbalanced, it is difficult to efficiently label the samples. Hence, we proposed an integrated active learning sampling strategy and designed an efficient framework for constructing speech emotion corpora in order to address the problems presented above. Comparing experiments with other active learning algorithms on 13 datasets, our method was shown to improve sampling efficiency. In addition, it is able to select small category samples to be labelled with preference in imbalanced datasets. During the actual corpus construction experiments, our method can prioritize selecting small class emotion samples. As even when the amount of labelled data is less than 50%, the accuracy rate still can reach 90%. This greatly enhances the efficiency of constructing the speech emotion corpus and fills in the gaps.

中文翻译：

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基于集成主动学习策略构建语音情感语料库的高效框架

近几十年来，由于机器学习的出现，语音情感识别得到了迅速发展。然而，缺乏语料库仍然是一个重要问题。实际的语音情感语料库构建，需要很多专业的演员在特定场景中表演各种情绪的声音。在数据标注过程中，由于不同情感类别的样本数量极度不平衡，很难对样本进行高效标注。因此，我们提出了一种集成的主动学习采样策略，并设计了一个构建语音情感语料库的有效框架，以解决上述问题。在 13 个数据集上与其他主动学习算法进行比较实验，我们的方法被证明可以提高采样效率。此外，它能够在不平衡的数据集中选择要标记的小类别样本。在实际的语料库构建实验中，我们的方法可以优先选择小类情感样本。即使标注数据量小于50%，准确率也能达到90%。这大大提高了构建语音情感语料库的效率，填补了空白。