- LM count files still undelivered!
- Cutting 50% of the weights, and then start to run into sticky with learning rate 0.0025. Completed after 6 iterations.
- The comparison shows very similar performance.
- Cut more weights based on up-to-now sparse model. Lead to iterative sparsity.
- Test on noisy data with the sparse.
Test on 100 hour data, structure 100_1200_1200_1200_1200_3580. Test on clean & 15db noiy speech.
|set||MFCC||GFCC||FB||MFCC + 15db||GFCC + 15db||FB + 15db|
- FB feature is much better than both MFCC and GFCC. Probably due to the less information lost without DCT.
- In noisy environment, GFCC obtains comparable or better performance compared to FB.
- We need to investigate how many FBs are the most appropriate.
- Inspired by the assumption of information lost with DCT, we need to test how another transform, LDA, leads to the similar information lost. We need to investigate which is the suitable dimension number for the LDA. We need to investigate non-linear discriminative approach which is simple but leads to less information lost.
- Another assumption for the better performance with FB is that the FB is more suitable for CMN. DCT accumulates a number of noisy channels and thus exhibits more uncertain. This in turn can hardly be normalized by CMN. We need to test the performance of FB and MFCC when no CMN is introduced.
- We can also test a simple 'the same dimension DCT'. If the performance is still worse than FB, we confirm that the problem is due to noisy channel accumulation.
- Need to investigate Gammatone filter banks. The same idea as FB, that we want to keep the information as much as possible. And it is possible to combine FB and GFB to pursue a better performance.
DNN Confidence estimation
- Lattice-based confidence show better performance with DNN with before.
- Accumulated DNN confidence is done. The confidence values are much more reasonable.
- Prepare MLP/DNN-based confidence integration.
Reading the table in the last section, we observe very disapointting performance reduction with noise. And we did not see too much advantage for FB and GFCC. We examine how if we introduce the noise in training. In this experiment, 15db noise are introduced in all the training data (100 hours), and the test utterances are in various noise level. Just give the performance on the test set online1. More performance is here:
- It is interesting to see that two factors are important in the noisy training: (1) speech should be clean (2) speech should match the training condition. The best performance is from 20db, which is not very clean and not very mismatch. This is interesting.
- We are looking forward to the noisy training which introduces some noises randomly in training.
- The interface for server-side is done. For embedded-side is on development.
- Fixed a bug which prompts intermediate results when short pause encountered.
- Fixed a CMN bug for the last segment.