“Lantian Li 2015-03-16”版本间的差异
来自cslt Wiki
（以“Weekly Summary 1. Explore the generalization of d-vector for text-indedenpent Speaker Recognition. The experimental results show that: d-vector performs better tha...”为内容创建页面） |
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| 第9行： | 第9行： | ||
The experimental results show that: global CMVN without speaker CMVN performs the best which meets the expectations. | The experimental results show that: global CMVN without speaker CMVN performs the best which meets the expectations. | ||
| − | 3. Train a text-content-based neural networks and extract d-vectors from these networks. But results show this method does not work. | + | 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. |
Next Week | Next Week | ||
1. Go on the task1 and task2. | 1. Go on the task1 and task2. | ||
2015年3月16日 (一) 09:14的最后版本
Weekly Summary
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.
Next Week
1. Go on the task1 and task2.
