关键词: 语音情感识别，坐标注意力机制，多头注意力机制，特定特征学习，共享特征学习

Abstract: Speech emotion recognition is an important and challenging task in human-computer interaction systems. To address the issues of single-feature representation and weak feature interaction in current speech emotion recognition systems, a multi-input interactive attention network is proposed. The network consists of two sub-networks, namely the Specific Feature Coordinate Residual Attention Network and the Shared Feature Multi-Head Attention Network. The former utilizes Res2Net and coordinate attention modules to learn specific features extracted from raw speech and generate multi-scale feature representations, enhancing the model's ability to represent emotion-related information. The latter combines features obtained from the forward network to form shared features, which are then input into the multi-head attention module. The multi-head attention mechanism allows for simultaneous attention to relevant information in different feature subspaces, enhancing the interaction among features and capturing highly discriminative characteristics. Through the synergistic effect of the two sub-networks mentioned above, the model can increase the diversity of features and improve the interaction capability among features. During the training process, a dual-loss function is applied for joint supervision, aiming to make the samples of the same class more compact and the samples of different classes more separated. The experimental results demonstrate that the proposed model achieves a weighted average accuracy of 91.43% on the EMO-DB database and 76.33% on the IEMOCAP database. Compared to other state-of-the-art models, it exhibits superior classification performance.

Key words: Speech emotion recognition, coordinate attention mechanism, multi-head attention mechanism, specific feature learning, shared feature learning