“2014-10-20”版本间的差异
来自cslt Wiki
（→Text Processing） |
|||
| （相同用户的3个中间修订版本未显示） | |||
| 第127行： | 第127行： | ||
====Domain specific LM==== | ====Domain specific LM==== | ||
| + | * lm based on baidu_hi and baiduzhidao is done, test on shujutang test set. | ||
| + | * weibo lm were training with pruning on counts(5,10,10,20,20),because it is too large. the ppl is twice as high than baidu_hi && baidu_zhidao. | ||
| − | + | * dongxu get good vocabulary from big data. Train 5-gram LM using Baiduzhidao_corpus(~30GB after preprocess) with new lexicon. There is a mistake when counted possiblity after merge. | |
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| + | ===tag LM=== | ||
| + | * use HIT's LTP tool to segment,pos and ner. the program is running(about 3 days) on baiduHi and baiduzhidao(total 365G) | ||
| + | * will use the small test set from xiaoxi for address-tag.. | ||
| + | * now about more 1M address,will prune it using frequency. | ||
===Word2Vector=== | ===Word2Vector=== | ||
| 第149行： | 第148行： | ||
* Knowledge vector started | * Knowledge vector started | ||
:* format the data | :* format the data | ||
| + | :* yuanbin will continue this work with help of xingchao. | ||
* Character to word conversion | * Character to word conversion | ||
| 第155行： | 第155行： | ||
* Google word vector train | * Google word vector train | ||
| − | :* | + | :* some ideal will discuss on weekly report. |
| − | + | ||
===RNN LM=== | ===RNN LM=== | ||
*rnn | *rnn | ||
| + | :* get baseline on nbest rescore of wer. | ||
*lstm+rnn | *lstm+rnn | ||
| − | : | + | :* trained the RNN+LSTM lm on wsj_np_data about 200M. the neural net work is 100*100(lstm cell)*10000 with 100 classes. it cost about 200 minutes each epoch on 2 cpu kernels. |
| − | : | + | :* get baseline on nbest rescore of wer. |
| + | :* more detail on LSTM | ||
===Translation=== | ===Translation=== | ||
* v3.0 demo released | * v3.0 demo released | ||
:* still slow | :* still slow | ||
| − | :* re-segment the word using new dictionary. | + | :* re-segment the word using new dictionary.will use the tencent-dic about 11w. |
:* check new data. | :* check new data. | ||
| 第172行： | 第173行： | ||
* search method: | * search method: | ||
| − | :* add the vsm and BM25 to improve the search. and the strategy of selecting the answer | + | :* add the vsm and BM25 to improve the search. and the strategy of selecting the answer. |
| − | :* | + | * spell check |
| − | * new inter will install SEMPRE | + | :* get ngram tool and make a simple demo. |
| + | :* get domain word list and pingyin tool from huilan. | ||
| + | ** new inter will install SEMPRE | ||
2014年10月20日 (一) 07:23的最后版本
Speech Processing
AM development
Contour
- NAN problem
- nan recurrence
------------------------------------------------------------ grid/atr. | Reproducible | add. ------------------------------------------------------------ grid-10 | yes | ------------------------------------------------------------ grid-12 | no | "nan" in different position ------------------------------------------------------------ grid-14 | yes | ------------------------------------------------------------
Sparse DNN
- Performance improvement found when pruned slightly
- Experiments show that
- Suggest to use TIMIT / AURORA 4 for training
RNN AM
- Initial test on WSJ , leads to out-memory.
- Using AURORA 4 short-sentence with a smaller number of targets.
Noise training
- First draft of the noisy training journal paper
- Paper Correction (Yinshi, Liuchao, Lin Yiye), be going.
Drop out & Rectification & convolutive network
- Drop out
- dataset:wsj, testset:eval92
std | dropout0.4 | dropout0.5 | dropout0.7 | dropout0.8
-------------------------------------------------------------
4.5 | 5.39 | 4.80 | 4.36 | -
- Test on noisy AURORA4 dataset
std | dropout0.4 | dropout0.5 | dropout0.7 | dropout0.8
-------------------------------------------------------------
6.05 | - | - | - | -
- Continue the droptout on normal trained XEnt NNET , eg wsj. (+)
- Draft the dropout-DNN weight distribution. (+)
- Rectification
- Still NAN error, need to debug. (+)
- MaxOut (+)
- Convolutive network
- Test more configurations
- Yiye will work on CNN
- Reading CNN tutorial
Denoising & Farfield ASR
- ICASSP paper submitted.
VAD
- Add more silence tag "#" in pure-silence utterance text(train).
- xEntropy model be training
- need to test baseline.
- Sum all sil-pdf as the silence posterior probability.
- Program done, to tune the threshold
Speech rate training
- Seems ROS model is superior to the normal one with faster speech
- Suggest to extract speech data of different ROS, construct a new test set(+)
- Suggest to use Tencent training data(+)
low resource language AM training
- Use Chinese NN as initial NN, change the last layer
- Various the used Chinese trained DNN layer numbers.
- feature_transform = 6000h_transform + 6000_N*hidden-layers
- Various the used Chinese trained DNN layer numbers.
nnet.init = random (4-N)*hidden-layers + output-layer | N / learn_rate | 0.008 | 0.001 | 0.0001 | | baseline | 17.00(14*2h) | | | | 4 | 17.75(9*0.6h) | 18.64 | | | 3 | 16.85 | | | | 2 | 16.69 | | | | 1 | 16.87 | | | | 0 | 16.88 | | |
- feature_transform = uyghur_transform + 6000_N*hidden-layers
nnet.init = random (4-N)*hidden-layers + output-layer Note: This is reproduced Yinshi's experiment | N / learn_rate | 0.008 | 0.001 | 0.0001 | | baseline | 17.00 | | | | 4 | 28.23 | 30.72 | 37.32 | | 3 | 22.40 | | | | 2 | 19.76 | | | | 1 | 17.41 | | | | 0 | | | |
- feature_transform = 6000_transform + 6000_N*hidden-layers
nnet.init = uyghur (4-N)*hidden-layers + output-layer | N / learn_rate | 0.008 | 0.001 | 0.0001 | | baseline | 17.00 | | | | 4 | 17.80 | 18.55 | 21.06 | | 3 | 16.89 | 17.64 | | | 2 | | | | | 1 | | | | | 0 | | | |
Scoring
- global scoring done.
- Pitch & rhythm done, need testing
- Harmonics program done, experiment to be done.
Confidence
- Reproduce the experiments on fisher dataset.
- Use the fisher DNN model to decode all-wsj dataset
Speaker ID
- Preparing GMM-based server.
Emotion detection
- Sinovoice is implementing the server
Text Processing
LM development
Domain specific LM
- lm based on baidu_hi and baiduzhidao is done, test on shujutang test set.
- weibo lm were training with pruning on counts(5,10,10,20,20),because it is too large. the ppl is twice as high than baidu_hi && baidu_zhidao.
- dongxu get good vocabulary from big data. Train 5-gram LM using Baiduzhidao_corpus(~30GB after preprocess) with new lexicon. There is a mistake when counted possiblity after merge.
tag LM
- use HIT's LTP tool to segment,pos and ner. the program is running(about 3 days) on baiduHi and baiduzhidao(total 365G)
- will use the small test set from xiaoxi for address-tag..
- now about more 1M address,will prune it using frequency.
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.
RNN LM
- rnn
- get baseline on nbest rescore of wer.
- lstm+rnn
- trained the RNN+LSTM lm on wsj_np_data about 200M. the neural net work is 100*100(lstm cell)*10000 with 100 classes. it cost about 200 minutes each epoch on 2 cpu kernels.
- get baseline on nbest rescore of wer.
- more detail on LSTM
Translation
- v3.0 demo released
- still slow
- re-segment the word using new dictionary.will use the tencent-dic about 11w.
- check new data.
QA
- search method:
- add the vsm and BM25 to improve the search. and the strategy of selecting the answer.
- spell check
- get ngram tool and make a simple demo.
- get domain word list and pingyin tool from huilan.
- new inter will install SEMPRE
