标题： 学习局部到全局特征聚合用于语音情感识别 显示英文标题

标题： Learning Local to Global Feature Aggregation for Speech Emotion Recognition

摘要： Transformer目前在语音情感识别(SER)中已经出现。 然而，其等分块不仅损害了频率信息，还忽略了跨帧的局部情感相关性，这些是表示情感的关键线索。 为了解决这个问题，我们提出了一个局部到全局特征聚合学习(LGFA)用于SER，可以在保留完整频率信息的同时，聚合不同尺度的帧内和段内的长期情感相关性，以增强话语级语音特征的情感区分能力。 为此，我们在段Transformer内部嵌套了一个帧Transformer。 首先，帧Transformer被设计用来挖掘帧之间的局部情感相关性以获得帧嵌入。 然后，将帧嵌入及其对应的段特征作为不同层次的补充输入到段Transformer中，以学习话语级的全局情感特征。 实验结果表明，LGFA的性能优于最先进方法。 显示英文摘要

摘要： Transformer has emerged in speech emotion recognition (SER) at present. However, its equal patch division not only damages frequency information but also ignores local emotion correlations across frames, which are key cues to represent emotion. To handle the issue, we propose a Local to Global Feature Aggregation learning (LGFA) for SER, which can aggregate longterm emotion correlations at different scales both inside frames and segments with entire frequency information to enhance the emotion discrimination of utterance-level speech features. For this purpose, we nest a Frame Transformer inside a Segment Transformer. Firstly, Frame Transformer is designed to excavate local emotion correlations between frames for frame embeddings. Then, the frame embeddings and their corresponding segment features are aggregated as different-level complements to be fed into Segment Transformer for learning utterance-level global emotion features. Experimental results show that the performance of LGFA is superior to the state-of-the-art methods.