“Lantian Li 14-11-24”版本间的差异
（以“Weekly Summary 1. Compare the performance between SVM and MLR, and the result is that MLR is worse than SVM. I think there are two reasons. 1/ the training datase...”为内容创建页面） |
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| 第19行： | 第19行： | ||
Next Week | Next Week | ||
| − | 1. | + | 1. Continue to look for distinguishing characteristics |
| − | + | 1) Improve K-means algorithm. | |
| − | + | 2) Implement the UBM segmentation score method. | |
| + | |||
| + | 3) Add original GMM score to feature vector. | ||
2014年11月24日 (一) 14:38的最后版本
Weekly Summary
1. Compare the performance between SVM and MLR, and the result is that MLR is worse than SVM.
I think there are two reasons. 1/ the training dataset is small.
2/ This issue based on GMM-UBM is not applied to complex non-linear model.
2. Compute the training accuarcy. For true speaker, the training accuray is about 4%, and for imp speaker, it is about 1%.
The EER is 2%. So there exists a difference between the true traning accuracy and imp training accuracy.
Now I still don't know whether to need to adjust the training dataset.
3. Help Jun Wang test the performance of PLDA-based classifier, results is baseline < SVM < DNN.
So I learn DNN method from him.
Next Week
1. Continue to look for distinguishing characteristics
1) Improve K-means algorithm.
2) Implement the UBM segmentation score method.
3) Add original GMM score to feature vector.
