Triplet Based Embedding Distance and Similarity Learning for Text-independent Speaker Verification
Table 1: Baseline x-vector system architecture
['Layer', 'Input-node', 'Input-dim', 'Output-dim']
The whole system consists of three parts and the training process is end-to-end. Firstly, 23-dimensional MFCC features are feed to the network as frame-level features. By the 5-layer time-delay neural networks (TDNNs), we can get the high-representation of frames. Then we compute the mean and standard deviation at the time dimension, and concatenate these two vectors, so-called statistics pooling layer. Fixed-dimensional utterance-level features are extracted through this operation. In the training stage, the network predicts the class of speaker by the last softmax layer with multi-class cross entropy loss. Besides, batch normalization and ReLU activation function are applied to all hidden layers. (1)