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.