“2014-11-25”版本间的差异
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| 500(490 less frequent and 10 unseen)||500|| 0.1 || 16.72 || 77.92 || - | | 500(490 less frequent and 10 unseen)||500|| 0.1 || 16.72 || 77.92 || - | ||
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| − | | | + | | || || 0.3 || 15.42 || 71.25 || - |
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2014年11月24日 (一) 04:57的版本
目录
Speech Processing
AM development
Environment
- Already buy 3 760GPU
- grid-9 760GPU crashed again; random freeze after s ; try to investigate the reason
- GPU problems on grid-17?
- disk (/work2) problem on grid-15
Sparse DNN
- Performance improvement found when pruned slightly
- need retraining for unpruned one; training loss
- The result of AURORA 4 will be available soon.
- details at http://liuc.cslt.org/pages/sparse.html
RNN AM
- 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.(+)
- details at http://liuc.cslt.org/pages/rnn.html
A new nnet training scheduler
- Initial code done. No better than original one considering of taking much more iterations.
- details at http://liuc.cslt.org/pages/nnet-sched.html
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
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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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- pretraining based maxout
- 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
- Data ready on tencent set; some errors on speech rate dependent model.
- Retrain new model
Scoring
- Timber Comparison done.
- harmonics based timber comparison: frequency based feature is better
- GMM based timber comparison is done. Similar to speaker recognition
- TODO: Code checkin and technique report.
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(need to discuss with xiaoxi)
- embedded language model(this week)
- train some more LMs with Zhenlong (dianzishu sogou bbs chosen)("need result").
- keep on training sogou2T lm(14/16 on 3rd iteration).(this week)
- new dict.
- handover of this work to hanzhenglong, give a simple docuemnt(this week)
tag LM
- different weight
| method | tag-jsgf | corpus | weight | wer | ser | add_wer |
|---|---|---|---|---|---|---|
| experiment 3 | 500(490 less frequent and 10 unseen) | 500 | 0.1 | 16.72 | 77.92 | - |
| 0.3 | 15.42 | 71.25 | - |
- problem
- check the code. the result is different on different work folder.(done)
- because the am is different
- need to do
- check the relation that between weight and size of dict.
- the short term should be punished.
- make a summary about tag-lm .
RNN LM
- rnn
- test RNNLM on Chinese data from jietong-data
- check the rnnlm code about how to Initialize and update learning rate.
- lstm+rnn
- check the lstm-rnnlm code about how to Initialize and update learning rate.
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
- give a basic report
- Character to word conversion
- prepare the task: word similarity
- prepare the dict.
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)
- need discuss it with duxk
improve fuzzy match
- add Synonyms similarity using MERT-4 method(hold)
improve lucene search
- 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
- demo (liurong two week)
.
- new inter will install SEMPRE
