Despite the recent progress in speech emotion recognition (SER), state-of-the-art systems are unable to achieve improved performance in cross-language settings. In this paper, we propose a Multimodal Dual Attention Transformer (MDAT) model to improve cross-language SER. Our model utilises pre-trained models for multimodal feature extraction and is equipped with a dual attention mechanism including graph attention and co-attention to capture complex dependencies across different modalities and achieve improved cross-language SER results using minimal target language data. In addition, our model also exploits a transformer encoder layer for high-level feature representation to improve emotion classification accuracy. In this way, MDAT performs refinement of feature representation at various stages and provides emotional salient features to the classification layer. This novel approach also ensures the preservation of modality-specific emotional information while enhancing cross-modality and cross-language interactions. We assess our model's performance on four publicly available SER datasets and establish its superior effectiveness compared to recent approaches and baseline models.

翻译：尽管语音情感识别（SER）近期取得了进展，但最先进的系统在跨语言场景下仍无法实现性能提升。本文提出一种多模态双重注意力变换器（MDAT）模型以改善跨语言SER。该模型利用预训练模型进行多模态特征提取，并配备包含图注意力和协同注意力的双重注意力机制，从而捕捉不同模态间的复杂依赖关系，使用极少量目标语言数据实现更优的跨语言SER结果。此外，模型还利用变换器编码器层进行高级特征表示，以提升情感分类准确率。通过这种方式，MDAT在不同阶段优化特征表示，并向分类层提供情感显著特征。这一创新方法在增强跨模态和跨语言交互的同时，确保了模态特异性情感信息的保留。我们在四个公开SER数据集上评估模型性能，验证了其相较于近期方法和基线模型的显著优越性。