How the embedding model (x-vectors) trained?
I read this paper:X-Vectors: Robust DNN Embeddings for Speaker Recognitionwhich describes how PyAnnote embedding block works.I’m not sure I understand how the X-Vector model was trained and tested:According to the paper, there is a DNN that was trained with N speakers (as classification task).Here’s the model:For embedding vectors, they exclude the last 2 layers of the DNN.They used LDA to reduce the dimensions of the embedding from 512 to 150 and run PLDA model.What is the “total context” in the model ?If they trained the DNN model with N-Speakers classification task, why do they need to run LDA + PLDA ?Are there any learn parameters on the LDA + PLDA step ?In order to produce an embedding vector, do we also need to run the LDA+PLDA step ?