对于语音的情感识别,针对单层长短期记忆(LSTM)网络在解决复杂问题时的泛化能力不足,提出一种嵌入自注意力机制的堆叠LSTM模型,并引入惩罚项来提升网络性能。对于视频序列的情感识别,引入注意力机制,根据每个视频帧所包含情感信息的多少为其分配权重后再进行分类。最后利用加权决策融合方法融合表情和语音信号,实现最终的情感识别。实验结果表明,与单模态情感识别相比,所提方法在所选数据集上的识别准确率提升4%左右,具有较好的识别结果。

A single-layer long short term memory (LSTM) network is not generalizable to solve complex speech emotion recognition problems. Therefore, a hierarchical LSTM model with a self-attention mechanism is proposed. Penalty items are introduced to improve network performance. For the emotion recognition of video sequences, the attention mechanism is introduced to assign a weight to each video frame according to its emotional information and then classify these frames. The weighted decision fusion method is used to fuse expressions and speech signals to achieve the final emotion recognition. The experimental results demonstrate that compared with single-modal emotion recognition, the recognition accuracy of the proposed method on the selected data is improved by approximately 4%, thus the proposed method has a better recognition results.