“2014-10-13”版本间的差异

来自cslt Wiki
跳转至： 导航、 搜索
（以“==Speech Processing == === AM development === ==== Sparse DNN ==== * Performance improvement found when pruned slightly * Experiments show that * Suggest to use TI...”为内容创建页面）
 
（撤销Zhangzy（讨论）的版本11926）
 
（2位用户的15个中间修订版本未显示）
第6行： 第6行：
 
* Experiments show that  
 
* Experiments show that  
 
* Suggest to use TIMIT / AURORA 4 for training
 
* 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====
 
====Noise training====
−
*
+
* First draft of the noisy training journal paper  
−
:* First draft of the noisy training journal paper  
+
* Paper Correction (Yinshi, Liuchao, Lin Yiye), be going.
−
:* Check abnormal behavior with large sigma (Yinshi, Liuchao)
+
  
 
====Drop out & Rectification & convolutive network====
 
====Drop out & Rectification & convolutive network====
  
 
* Drop out
 
* Drop out
 +
:* dataset:wsj, testset:eval92
 +
        std |  dropout0.2 | dropout0.4 | dropout0.6 | dropout0.8
 +
    -------------------------------------------------------------
 +
      4.5  |    4.54    |    4.5    |  4.25    |    4.5     
  
−
:* No performance improvement found yet.
+
:* Test on noisy AURORA 4 dataset
−
:* [http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=wangd&step=view_request&cvssid=261]
+
:* Continue the droptout on normal trained XEnt NNET , eg wsj.  
 +
:* Draft the dropout-DNN weight distribution.
  
 
* Rectification
 
* Rectification
−
:* Dropout NA problem was caused by large magnitude of weights
+
:* Still NAN error, need to debug.
 +
 
 +
* MaxOut
  
 
* Convolutive network
 
* Convolutive network
−
# Test more configurations  
+
:*Test more configurations  
−
* Zhiyong will work on CNN
+
:* Yiye will work on CNN
−
 
+
−
* Recurrent neural network
+
−
:* investigate CURRENNT for AM
+
−
 
+
−
 
+
  
 
====Denoising & Farfield ASR====
 
====Denoising & Farfield ASR====
  
−
*  
+
* ICASSP paper submitted.
−
* Lasso-based de-reverberation is done with the REVERBERATION toolkit
+
−
:* Start to compose the experiment section for the SL paper.
+
  
 
====VAD====
 
====VAD====
  
−
* problems found at the beginning part of speech (0-0.02s?)
+
* Add more silence tag "#" in pure-silence utterance text(train).
−
* Noise model training done. Under testing.
+
:* xEntropy model be training
−
* Need to investigate the performance reduction in babble noise. Call Jia.
+
  
 +
* Sum all sil-pdf as the silence posterior probability.
  
 
====Speech rate training====
 
====Speech rate training====
 
*  
 
*  
−
* Some interesting results with the simple speech rate change algorithm was obtained on the WSJ db
 
−
[http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=wangd&step=view_request&cvssid=268]
 
−
 
 
* Seems ROS model is superior to the normal one with faster speech
 
* Seems ROS model is superior to the normal one with faster speech
 
* Need to check distribution of ROS on WSJ
 
* Need to check distribution of ROS on WSJ
第56行： 第56行：
  
 
==== low resource language AM training ====
 
==== low resource language AM training ====
−
* Results on CVSS[http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?step=view_request&cvssid=274]
 
 
* Use Chinese NN as initial NN, change the last layer
 
* Use Chinese NN as initial NN, change the last layer
 +
:* Various the used Chinese trained DNN layer numbers. 
  
 
====Scoring====
 
====Scoring====
−
 
 
* global scoring done.
 
* global scoring done.
 
* Pitch & rhythm done, need testing
 
* Pitch & rhythm done, need testing
第67行： 第66行：
  
 
====Confidence====
 
====Confidence====
 +
* Reproduce the experiments on fisher dataset.
 +
* Use the fisher DNN model to decode all-wsj dataset
  
−
* experiments done, need more data
 
−
*
 
−
* Basic confidence by using lattice-based posterior + DNN posterior + ROS done
 
−
* 23% detection error achieved by balanced model
 
  
 
===Speaker ID===
 
===Speaker ID===
−
 
+
* Preparing GMM-based server.
−
* Add VAD to system
+
−
* GMM-based test program delivered
+
−
* GMM registration program done
+
  
 
===Emotion detection===
 
===Emotion detection===
−
* Zhang Weiwei is learning the code
+
 
 
* Sinovoice is implementing the server
 
* Sinovoice is implementing the server
  
第110行： 第104行：
  
 
* Knowledge vector started
 
* Knowledge vector started
−
:* document obtained from wiki
+
:* format the data
−
:* formula obtained
+
  
 
* Character to word conversion
 
* Character to word conversion
−
:* read more paper .
+
:* prepare the task: word similarity
−
:* prepare to train .
+
:* prepare the dict.
  
 
* Google word vector train
 
* Google word vector train
第121行： 第114行：
  
 
===RNN LM===
 
===RNN LM===
−
 
+
*rnn
−
* Prepare WSJ database
+
*lstm+rnn
−
* Trained model 10000 x 4 + 320 + 10000
+
: install the tool and prepare the data of wsj
−
* Better performance obtained (4.16-3.47)
+
: prepare the baseline.
−
* gigaword sampling for Chinese data
+
−
 
+
 
===Translation===
 
===Translation===
  
 
* v3.0 demo released
 
* v3.0 demo released
 
:* still slow
 
:* still slow
−
:* cut the vocabulary that is not important .
+
:* re-segment the word using new dictionary.
 +
:* check new data.
  
 
===QA===
 
===QA===
  
−
* liangshan_v1 performance 74.3%.
+
* search method:
−
* New framework and GA method is done
+
:* add the vsm and BM25 to improve the search. and the strategy of selecting the answer
−
* add SEMPRE tool to framework
+
:* segment the word using minimum granularity for lucene index and bag-of-words method.
 +
* new inter will install SEMPRE

2014年10月20日 (一) 06:31的最后版本

Speech Processing

AM development

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 |  dropout0.2 | dropout0.4 | dropout0.6 | dropout0.8
    ------------------------------------------------------------- 
      4.5  |     4.54    |    4.5     |   4.25     |    4.5
  • Test on noisy AURORA 4 dataset
  • 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

Denoising & Farfield ASR

  • ICASSP paper submitted.

VAD

  • Add more silence tag "#" in pure-silence utterance text(train).
  • xEntropy model be training
  • Sum all sil-pdf as the silence posterior probability.

Speech rate training

  • Seems ROS model is superior to the normal one with faster speech
  • Need to check distribution of ROS on WSJ
  • Suggest to extract speech data of different ROS, construct a new test set
  • Suggest to use Tencent training data
  • Suggest to remove silence when compute ROS

low resource language AM training

  • Use Chinese NN as initial NN, change the last layer
  • Various the used Chinese trained DNN layer numbers.

Scoring

  • global scoring done.
  • Pitch & rhythm done, need testing
  • Harmonics hold


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

h2. ngram generation is on going h2. look the memory and baidu_hi done

h2. NUM tag LM:

  • maxi work is released.
  • yuanbin continue the tag lm work.
  • add the ner to tag lm .
  • Boost specific words like wifi if TAG model does not work for a particular word.


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
  • Character to word conversion
  • prepare the task: word similarity
  • prepare the dict.
  • Google word vector train
  • improve the sampling method

RNN LM

  • rnn
  • lstm+rnn
install the tool and prepare the data of wsj
prepare the baseline.

Translation

  • v3.0 demo released
  • still slow
  • re-segment the word using new dictionary.
  • check new data.

QA

  • search method:
  • add the vsm and BM25 to improve the search. and the strategy of selecting the answer
  • segment the word using minimum granularity for lucene index and bag-of-words method.
  • new inter will install SEMPRE
取自“http://index.cslt.org/mediawiki/index.php?title=2014-10-13&oldid=11940”