Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers
This addresses the challenge of cross-language SER for applications in multilingual settings, representing an incremental improvement with a novel hybrid method.
The paper tackles the problem of cross-language speech emotion recognition (SER) by proposing a Multimodal Dual Attention Transformer (MDAT) model, which achieves improved performance using minimal target language data, as demonstrated on four publicly available SER datasets with superior results compared to recent approaches and baselines.
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.