2013-09-13
来自cslt Wiki
目录
Data sharing
- LM count files still undelivered!
DNN progress
Sparse DNN
- Cutting 50% of the weights, and then start to run into sticky with learning rate 0.0025. Continuous pruning until 1.6% weights left.
- The test results show not much gains with noise.
- 1/8 sparsity shows no evident performance reduction, as we observed before and is consistent the results reported by MS.
FBank features
- CMN shows similar impact to MFCC & FBank. Since MFCC involves summary of various random channels, the mean and covariance of the dimensions are less random. This leads to two possible impacts: first, the dimensions are relatively stable therefore CMVN does not contribute much; on other hand, estimation of mean and variance is more accurate so CMVN leads to more reliable results. This means CMVN leads to unpredictable performance improvement for MFCC & Fbank, depending on the data set.
- Choose various Fbank dimension, keep LDA output dimension as 100. FB30 seems the best.
- Choose FBank 40, test various LDA output dimension. The results show LDA is still helpful, and dimension 200 is sufficient.
- 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.
Tencent exps
N/A
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.
Noisy training
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:
| SNR | MFCC | GFCC |
|---|---|---|
| clean | 45.63 | 38.12 |
| 20db | 32.41 | 30.54 |
| 15db(matched training) | 35.05 | 32.80 |
| 10db | 41.06 | 38.53 |
- 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.
Stream decoding
- 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.
