Lantian Li 2015-03-16
1. Explore the generalization of d-vector for text-indedenpent Speaker Recognition.
The experimental results show that: d-vector performs better than i-vector only under cosine distance. While LDA and PLDA do not work for d-vector.
2. Explore the impact of CMNV on the d-vector for Speaker Recognition.
The experimental results show that: global CMVN without speaker CMVN performs the best which meets the expectations.
3. Train a text-content-based (block-mask) neural networks and extract d-vectors from these networks. But results show this method does not work.
1. Go on the task1 and task2.