为了在说话人识别任务中有效融合音视频特征,提出一种基于深度门的多模态长短期记忆(LSTM)网络。首先对每一类单独的特征建立一个多层LSTM模型,并通过深度门连接上下层的记忆存储单元,增强上下层的联系,提升该特征本身的分类性能。同时,通过在不同模型之间共享连接隐藏层输出与各个门单元的权重,学习每一层模型之间的联系。实验结果表明,该方法能有效融合音视频特征,提高说话人识别的准确率,并且对干扰具有一定的稳健性。

In order to effectively fuse the audio and visual features in the task of speaker recognition, a multimodal long short-term memory network (LSTM) with depth-gate is proposed. First, a multi-layer LSTM model is established for each type of individual features. Then the depth-gate is used to connect the memory cells in the upper and lower layers, and the connection between the upper and lower layers is enhanced, which improves the classification performance of the feature itself. At the same time, the connection among layer models can be learned by sharing the output of hidden layers and the weight of each gate unit among different models. The experimental results show that this method can be used to effectively fuse the audio and video features and improve the accuracy of speaker recognition. Moreover, this method is robust to external disturbance.