半监督跨语言语音情感识别
Semi-supervised cross-lingual speech emotion recognition
AI总结：
针对跨语言语音情感识别中源域与目标域分布差异及标注数据稀缺的问题，提出基于Transformer的半监督学习方法，利用硬伪标签和软伪标签策略适应新域，在五种语言上未加权准确率平均提升40%。
摘要：
得益于深度学习技术的使用，近年来单语言语音情感识别（SER）的性能得到了极大提升。然而，由于两个主要因素，跨语言 SER 在实际应用中仍然是一个挑战：首先是源域和目标域分布之间存在巨大差距；第二个因素是与新语言的有标签话语相比，无标签话语的大量存在。考虑到上述情况，我们提出了一种半监督学习（SSL）方法，用于在目标域（即新语言）中仅有少量标注样本可用时的跨语言情感识别。我们的方法基于 Transformer，并通过在无标签话语上利用伪标签策略来适应新域。具体而言，我们研究了硬伪标签和软伪标签方法的使用。我们在说话人无关的设置下，分别在源语言和新语言上全面评估了所提方法的性能，并展示了其在属于不同语系的五种语言上的鲁棒性。实验结果表明，与最先进的方法相比，未加权准确率平均提高了 40%。
英文摘要：
Performance in Speech Emotion Recognition (SER) on a single language has increased greatly in the last few years thanks to the use of deep learning techniques. However, cross-lingual SER remains a challenge in real-world applications due to two main factors: the first is the big gap among the source and the target domain distributions; the second factor is the major availability of unlabeled utterances in contrast to the labeled ones for the new language. Taking into account previous aspects, we propose a Semi-Supervised Learning (SSL) method for cross-lingual emotion recognition when only few labeled examples in the target domain (i.e. the new language) are available. Our method is based on a Transformer and it adapts to the new domain by exploiting a pseudo-labeling strategy on the unlabeled utterances. In particular, the use of a hard and soft pseudo-labels approach is investigated. We thoroughly evaluate the performance of the proposed method in a speaker-independent setup on both the source and the new language and show its robustness across five languages belonging to different linguistic strains. The experimental findings indicate that the unweighted accuracy is increased by an average of 40% compared to state-of-the-art methods.
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