2014-11-10
来自cslt Wiki
目录
Speech Processing
AM development
Environment
- Already buy 3 760GPU
- grid-9 760GPU crashed again
Sparse DNN
[link here]
- Performance improvement found when pruned slightly
- The result of AURORA 4 will be available soon.
RNN AM
[link here]
- Initial nnet seems not very well, need to be pre-trained or test lower learn-rate.
- For AURORA 4 1h/epoch, model train done.
- Using AURORA 4 short-sentence with a smaller number of targets.(+)
- Adjusting the learning rate.(+)
- Trying toolkit of Microsoft.(+)
A new nnet training scheduler
[link here]
- Initial code done. No better than original one considering of taking much more iterations.
Noise training
- Paper has been submitted.
Drop out & Rectification & convolutive network
- Drop out
- dataset:wsj, testset:eval92
std | dropout0.4 | dropout0.5 | dropout0.6 | dropout0.7 | dropout0.7_iter7(maxTr-Acc) | dropout0.8 | dropout0.8_iter7(maxTr-Acc)
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4.5 | 5.39 | 4.80 | 4.75 | 4.36 | 4.39 | 4.55 | 4.71
- Frame-accuarcy seems not consistent with WER. Using the train-data as cv, verify the learning ability of the model.
Seems in one nnet model the train top frame accuracy is not consistent with the WER.
- Decode test_clean_wv1 dataset.
- AURORA4 dataset
(1) Train: train_nosiy
drop-retention/testcase(WER) | test_clean_wv1 | test_airport_wv1 | test_babble_wv1 | test_car_wv1
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std-baseline | 9.60 | 11.41 | 11.63 | 8.64
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dp-0.3 | 12.91 | 16.55 | 15.37 | 12.60
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dp-0.4 | 11.48 | 14.43 | 13.23 | 11.04
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dp-0.5 | 10.53 | 13.00 | 12.89 | 10.24
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dp-0.6 | 10.02 | 12.32 | 11.81 | 9.29
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dp-0.7 | 9.65 | 12.01 | 12.09 | 8.89
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dp-0.8 | 9.79 | 12.01 | 11.77 | 8.91
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dp-1.0 | 9.94 | 11.33 | 12.05 | 8.32
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baseline_dp0.4_lr0.008 | 9.52 | 12.01 | 11.75 | 9.44
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baseline_dp0.4_lr0.0001 | 9.92 | 14.22 | 13.59 | 10.24
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baseline_dp0.4_lr0.00001 | 9.06 | 13.27 | 13.14 | 9.33
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baseline_dp0.8_lr0.008 | 9.16 | 11.23 | 11.42 | 8.49
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baseline_dp0.8_lr0.0001 | 9.22 | 11.52 | 11.77 | 8.82
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baseline_dp0.8_lr0.00001 | 9.12 | 11.27 | 11.65 | 8.68
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dp-0.4_follow-std-lr | 11.33 | 14.60 | 13.50 | 10.95
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dp-0.8_follow-std-lr | 9.77 | 12.01 | 11.79 | 8.93
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dp-0.4_4-2048 | 11.69 | 16.13 | 14.24 | 11.98
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dp-0.8_4-2048 | 9.46 | 11.60 | 11.98 | 8.78
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- Test with AURORA4 of 7000 (clean + noisy).
- Follow the standard DNN training learn-rate to avoid the different learn-rate changing time of various DNN training. Similar performance is obtained.
- Find and test unknown noise test-data.(+)
- Have done the droptout on normal trained XEnt NNET , eg wsj(learn-rate:1e-4/1e-5). Seems small learn-rate get the balance of accuracy and train-time.
- Draft the dropout-DNN weight distribution. (++)
- Rectification
- Combine drop out and rectifier.(+)
- Change the learn-rate in the middle of the training, Modify the train_nnet.sh script(Liu Chao).
- MaxOut
- 6min/epoch
1) AURORA4 -15h
NOTE: gs==groupsize
(1) Train: train_clean
model/testcase(WER) | test_clean_wv1 | test_airport_wv1 | test_babble_wv1 | test_car_wv1
---------------------------------------------------------------------------------------------------------
std-baseline | 6.04 | 29.91 | 27.76 | 16.37
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lr0.008_gs6 | -
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lr0.008_gs10 | -
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lr0.008_gs20 | -
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lr0.008_l1-0.01 | -
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lr0.008_l1-0.001 | -
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lr0.008_l1-0.0001 | -
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lr0.008_l1-0.000001 | -
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lr0.008_l2-0.01 | -
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lr0.006_gs10 | -
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lr0.004_gs10 | -
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lr0.002_gs10 | 6.21 | 28.48 | 27.30 | 16.37
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lr0.001_gs1 | -
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lr0.001_gs2 | -
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lr0.001_gs4 | -
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lr0.001_gs6 | 6.04 | 25.17 | 24.31 | 14.19
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lr0.001_gs8 | 5.85 | 25.72 | 24.35 | 14.28
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lr0.001_gs10 | 6.23 | 27.04 | 25.51 | 14.22
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lr0.001_gs15 | 5.94 | 30.10 | 27.53 | 19.00
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lr0.001_gs20 | 6.32 | 28.10 | 26.47 | 16.98
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- P-norm
- Convolutive network (+)
- AURORA 4
| wer | hid-layers | hid-dim | delta-order | splice | lda-dim | learn-rate | pooling | TBA ----------------------------------------------------------------------------------------------------------------------- cnn_std_baseline| 6.70 | 4 | 1200 | 0 | 4 | 198 | 0.008 | 3 |patch-dim1 7 ----------------------------------------------------------------------------------------------------------------------- cnn_std_1000_3 | 6.61 | 4 | 1000 | 0 | 4 | 198 | 0.008 | 3 |patch-dim1 7 ----------------------------------------------------------------------------------------------------------------------- cnn_std_1400_3 | 6.61 | 4 | 1400 | 0 | 4 | 198 | 0.008 | 3 |patch-dim1 7 ----------------------------------------------------------------------------------------------------------------------- cnn_std_1200_4 | 6.91 | 4 | 1200 | 0 | 4 | 198 | 0.008 | 4 |patch-dim1 6 ----------------------------------------------------------------------------------------------------------------------- cnn_std_1200_2 | - | 4 | 1200 | 0 | 4 | 198 | 0.008 | 2 |patch-dim1 8 ----------------------------------------------------------------------------------------------------------------------- cnn_std_1200_3 | 6.66 | 5 | 1200 | 0 | 4 | 198 | 0.008 | 3 |patch-dim1 7 -----------------------------------------------------------------------------------------------------------------------
- READ paper
Denoising & Farfield ASR
- ICASSP paper submitted.
- HOLD
VAD
- Frame energy feature extraction, done
- Harmonics and Teager energy features being investigation
- Previous results to be organized for a paper
Speech rate training
- 100h random select from 1000h tec dataset
- baseline and ROS NNet train done, will decoding soon
- Seems ROS model is superior to the normal one with faster speech
low resource language AM training
- HOLD
- Uyghur language model has been released to JT. Done.
Scoring
- Timber Comparison on testing
Confidence
- Reproduce the experiments on fisher dataset.
- Use the fisher DNN model to decode all-wsj dataset
- preparing scoring for puqiang data
Speaker ID
- Preparing GMM-based server.
- EER ~ 11.2% (GMM-based system)
- test different number of components; fast i-vector computing
Language ID
- GMM-based language is ready.
- Delivered to Jietong
Emotion detection
- Sinovoice is implementing the server
Text Processing
LM development
Domain specific LM
- domain lm
- weibo lm with pruning 0 10 10 20 20 testing done. weibo lm with pruning 0 10 8 8 8 under testing. weibo lm without pruning 4/8 done.
- merger weibo、baiduhi and baiduzhidao lm and test (this week)
- confirm the size of alpa with xiaomin for business application.(like e-13)
- get the general test data from miaomin .this test set may get from online.
- new dict.
- Tested the earlier vocabulary on 6000.txt with ppl.
old150K new166K new150K baiduzhidao 394 369 333 baiduhi 217 190 188
- Built new 100K,150K,200K vocabulary
- Had fixed some bugs in sogou dict spider.
- new toolkit:find method to update the new dict. can get new wordlist from sougou and get word information from baidu.(two week)
tag LM
- set new test
| method | baeline | weight0.1 | weight0.5 | weight1 | weight2 | weight3 |
|---|---|---|---|---|---|---|
| wer | 56.58 | 69.49 | 62.23 | 58.03 | 56.90 | - |
| "北京" | 6/10 | 4/10 | 4/10 | 2/10 | 1/10 | 0 |
| detail | 288 ins, 5075 del, 3178 sub | 196 ins, 6016 del, 4278 sub | 190 ins, 5870 del, 3334 sub | 243 ins, 5294 del, 3223 sub | 344 ins, 4558 del, 3687 sub | - |
- mix seed lm with big lm, test address-tag on big lm
RNN LM
- rnn
- RNNLM=>ALPA make a report
- test RNNLM on Chinese data from jietong-data
- check the rnnlm code.
- lstm+rnn
- check the lstm-rnnlm code
Word2Vector
W2V based doc classification
- Initial results variable Bayesian GMM obtained. Performance is not as good as the conventional GMM.
- Non-linear inter-language transform: English-Spanish-Czch: wv model training done, transform model on investigation
- SSA-based local linear mapping still on running.
- k-means classes change to 2.
- Knowledge vector started
- format the data
- yuanbin will continue this work with help of xingchao.
- Character to word conversion
- prepare the task: word similarity
- prepare the dict.
- Google word vector train
- some ideal will discuss on weekly report.
Translation
- v4.0 demo released
- cut the dict and use new segment-tool
QA
deatil:[1]
Spell mistake
- retrain the ngram model(caoli)
- prepare the test and development set(caoli)
improve fuzzy match
- add Synonyms similarity using MERT-4 method
improve lucene search
- our vsm method
| method | lucene | vsm_idf(haiguan) | VSM_idf(baidu) | vsm_idf(tain) | vsm_idf(calculate) |
|---|---|---|---|---|---|
| Accary | 0.6628 | 0.6228 | 0.6197 | 0.5827 | 0.5426 |
- lucene top
- top10(82.95%),top20(86.34),top50(90.23%),top100(94.11%),top200(96.18%),top1000(97.31%),top2000(97.87%),top5000(98.75%),top10000(99.06)
- test the result of top(100,200,1000) in full qa(lucene+fuzzymatch)(caoli)
- lucene Optimization(liurong)
- rewrite the method to select the 50 standard question not same template.(liurong)
- check the word segment for template.(liurong)
- boost the query keyword using IDF
| method | Default | idf_train | idf_train_norm | idf_baidu | idf_baidu_norm |
|---|---|---|---|---|---|
| Accary | 0.66228 | 0.651629 | 0.57644 | 0.647869 | 0.65288 |
- using MERT-4 method to get good value of multi-feature.like IDF,NER,baidu_weight,keyword etc.(liurong this month)
Multi-Scene Recognition
- add the triples search to QA engine
- discuss the detail and give a report.(liurong)
- demo (liurong two week)
.
- new inter will install SEMPRE
