“ASR:2014-12-15”版本间的差异
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:* Finish the baseline and compared with simple factorization method based on co-occurrence. | :* Finish the baseline and compared with simple factorization method based on co-occurrence. | ||
:* Visualization of knowledge vector[http://cslt.riit.tsinghua.edu.cn/mediawiki/index.php/%E6%96%87%E4%BB%B6:Shades_of_color.pdf] | :* Visualization of knowledge vector[http://cslt.riit.tsinghua.edu.cn/mediawiki/index.php/%E6%96%87%E4%BB%B6:Shades_of_color.pdf] | ||
+ | * test | ||
+ | :* set up the test data. | ||
====relation==== | ====relation==== | ||
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* add Synonyms similarity using MERT-4 method(hold) | * add Synonyms similarity using MERT-4 method(hold) | ||
====improve lucene search==== | ====improve lucene search==== | ||
− | + | * domain keyword to improve search in lucene using mert | |
− | :* | + | :* keyword in sentence,like frequency ,position. |
+ | :* keyword like pos,parser information. | ||
+ | |||
====XiaoI framework==== | ====XiaoI framework==== | ||
− | * | + | * context in QA |
====query normalization==== | ====query normalization==== | ||
* using NER to normalize the word | * using NER to normalize the word | ||
− | + | * | |
* new inter will install SEMPRE | * new inter will install SEMPRE |
2014年12月22日 (一) 06:13的最后版本
目录
Speech Processing
AM development
Environment
- Already buy 3 760GPU
- grid-9/12 760GPU crashed again; grid-11 shutdown automatically.
- Change 760gpu card of grid-12 and grid-14(+).
- First down-frequency of gpu760.
Sparse DNN
- Performance improvement found when pruned slightly
- need retraining for unpruned one; training loss
- 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/rnnam.html
- Reading papers
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
- Test on 4000h dataset.
Dropout & Maxout & Convolutive network
- Drop out(+)
- Use different proportion of noise data to investigate the effect of xEnt and mpe and dropout
- Problem 1) The effect of dropout in different noise proportion;
- Use different proportion of noise data to investigate the effect of xEnt and mpe and dropout
2) The effect of MPE in different noise proportion; 3) The effect of MPE+dropout in different noise proportion.
Dropout is effective for minority.
- Find and test unknown noise test-data.(++)
- MaxOut
- Pretraining based maxout, can't use large learning-rate.
- P-norm
- Need to solve the too small learning-rate problem
- Add one normalization layer after the pnorm-layer
- Add L2-norm upper bound
- Need to solve the too small learning-rate problem
- Convolutive network (+)
DAE(Deep Atuo-Encode)
(1) train_clean drop-retention/testcase(WER)| test_clean_wv1 | test_airport_wv1 | test_babble_wv1 | test_car_wv1 --------------------------------------------------------------------------------------------------------- std-xEnt-sigmoid-baseline| 6.04 | 29.91 | 27.76 | 16.37 --------------------------------------------------------------------------------------------------------- std+dae_cmvn_noFT_2-1200 | 7.10 | 15.33 | 16.58 | 9.23 --------------------------------------------------------------------------------------------------------- std+dae_cmvn_splice5_2-100 | 8.19 | 15.21 | 15.25 | 9.31 ---------------------------------------------------------------------------------------------------------
- test on XinWenLianBo music. results on
Denoising & Farfield ASR
- ICASSP paper submitted.
- HOLD
VAD
- Harmonics and Teager energy features being investigation (++)
Speech rate training
- Data ready on tencent set; some errors on speech rate dependent model. error fixed.
- Retrain new model(+)
Scoring
- Timber Comparison done.
- harmonics based timber comparison: frequency based feature is better. done
- GMM based timber comparison is done. Similar to speaker recognition. done
- TODO: Code checkin and technique report. done
Confidence
- Reproduce the experiments on fisher dataset.
- Use the fisher DNN model to decode all-wsj dataset
- preparing scoring for puqiang data
- HOLD
Speaker ID
- Preparing GMM-based server.
- EER ~ 4% (GMM-based system)--Text independent
- EER ~ 6%(1s) / 0.5%(5s) (GMM-based system)--Text dependent
- test different number of components; fast i-vector computing
- Test with number recordings, The 256 number component is the best.
- Test with text-dependent recordings, The 1024 number component is the best.
- Interpolation alpha is not sensitive.
Language ID
- GMM-based language is ready.
- Delivered to Jietong
- Prepare the test-case
Voice Conversion
- Yiye is reading materials(+)
Text Processing
LM development
Domain specific LM
- domain lm
- Sougou2T : kn-count continue .
- lm v2.0.
- problem that the result is different between kaldi and jitong.
- check the format of data encode in kaldi and jitong.
tag LM
- paper(two week)
- add more related work in introduction and rich the paper
RNN LM
- rnn
- test wer RNNLM on Chinese data from jietong-data(this week)
- generate the ngram model from rnnlm and test the ppl with different size txt.[1]
- lstm+rnn
- check the lstm-rnnlm code about how to Initialize and update learning rate.(hold)
Word2Vector
W2V based doc classification
- Initial results variable Bayesian GMM obtained. Performance is not as good as the conventional GMM.(hold)
- Non-linear inter-language transform: English-Spanish-Czch: wv model training done, transform model on investigation
Knowledge vector
- baseline
- Debug the training part and add normalization, result in cvss[326].
Category:Mammal orders entry number: 15572 testset: 70 couples (28 data scored 0, 11 data scored 1, 16 data scored 2,15 data scored 3) Spearman rank-order coefficient | Pearson correlation coefficient Factorization: 0.698714 0.747226 Baseline: 0.721214 0.778838 testset: 70 couples (7 data scored 0, 2 data scored 1, 5 data scored 2, 8 data scored 3) Baseline:(category) 0.523433
- Finish the baseline and compared with simple factorization method based on co-occurrence.
- Visualization of knowledge vector[2]
- test
- set up the test data.
relation
- Accomplish transE with almost the same performance as the paper did(even better)[3]
Character to word
- Character to word conversion(hold)
- prepare the task: word similarity
- prepare the dict.
Translation
- v5.0 demo released
- cut the dict and use new segment-tool
QA
improve fuzzy match
- add Synonyms similarity using MERT-4 method(hold)
improve lucene search
- domain keyword to improve search in lucene using mert
- keyword in sentence,like frequency ,position.
- keyword like pos,parser information.
XiaoI framework
- context in QA
query normalization
- using NER to normalize the word
- new inter will install SEMPRE