一种基于领域自适应的跨语言元学习语音情感识别方法
A Cross-Lingual Meta-Learning Method Based on Domain Adaptation for Speech Emotion Recognition
AI总结：
针对低资源语言语音情感识别数据稀缺问题，提出结合大型预训练骨干网络、原型网络的改进多阶段元学习方法，通过元测试微调技术提升分布外性能，在希腊语、罗马尼亚语小样本任务中取得优异准确率。
摘要：
性能最优的语音模型需在大量目标语言数据上训练，但多数语言数据稀疏，模型训练难度大，这种数据短缺问题在语音情感识别领域更为突出。本研究探讨了数据有限场景下，尤其是语音情感识别任务的模型性能。元学习擅长提升小样本学习效果，因此我们将元学习技术应用于语音情感识别、口音识别和说话人识别任务。为此，我们对多阶段元学习方法提出了一系列改进。不同于其他研究因元学习算法计算成本高而聚焦于小模型，我们采用了更具实用性的方案：引入大型预训练骨干网络和原型网络，使方法更具可行性与适用性。我们最突出的贡献是提出了一种改进的元测试阶段微调技术，能显著提升模型在分布外数据集上的性能。结合其他多项改进带来的增量提升，在4-way 5-shot学习设置下，我们的方法在未纳入训练集或验证集的希腊语和罗马尼亚语语音情感识别数据集上，分别取得了83.78%和56.30%的准确率。
英文摘要：
Best-performing speech models are trained on large amounts of data in the language they are meant to work for. However, most languages have sparse data, making training models challenging. This shortage of data is even more prevalent in speech emotion recognition. Our work explores the model's performance in limited data, specifically for speech emotion recognition. Meta-learning specializes in improving the few-shot learning. As a result, we employ meta-learning techniques on speech emotion recognition tasks, accent recognition, and person identification. To this end, we propose a series of improvements over the multistage meta-learning method. Unlike other works focusing on smaller models due to the high computational cost of meta-learning algorithms, we take a more practical approach. We incorporate a large pre-trained backbone and a prototypical network, making our methods more feasible and applicable. Our most notable contribution is an improved fine-tuning technique during meta-testing that significantly boosts the performance on out-of-distribution datasets. This result, together with incremental improvements from several other works, helped us achieve accuracy scores of 83.78% and 56.30% for Greek and Romanian speech emotion recognition datasets not included in the training or validation splits in the context of 4-way 5-shot learning.
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。
