“Zhiyong Zhang”版本间的差异

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=Task To Do=
 
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* 1, RNN speech recognition (Tied-context-dependent-state and End-to-End)
 
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* 2, Real environment noise cancellation(DNN-DAE/CNN-DAE/RNN-DAE: echo or reverberation)
 
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* 3, Integrate the class information to HCLG fst for speech recognition
 
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* 4, Multi-Mode features based VAD
 
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* 5, DNN based Language identification and Speaker identification
 
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* 6, Distant speech recognition(Reverberation, Mutli-microphones)
 
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* 7, Voice conversation
 
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* 8, Unbound activation function(Rectifier/Maxout/Pnorm) go-through searching method.
 
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* 9, Sparse DNN
 
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* 10, Neural network visulization
 
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* 11, DAE+dropout
 
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=Technical Report To Write=
 
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* 1, DNN-DAE based noise cancellation
 
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* 2, Speech Rate DNN speech recognition
 
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* 3, CNN+fbank feature combination
 
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* 4, Uyghur low-resource acoustic model enhancement
 
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* 5, Uyghur 20h database release
 
  
 
=Papers To Read =
 
=Papers To Read =
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* Testing on 100h-Ch+100h-En, better performance observed.
 
* Testing on 100h-Ch+100h-En, better performance observed.
 
* Now testing the source code on 1400h_8k data, but stange decoding results got.Need to further investigate.
 
* Now testing the source code on 1400h_8k data, but stange decoding results got.Need to further investigate.
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=Reading Lists=
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*[[媒体文件:Efficient_mini-batch_training_for_stochastic_optimization.pdf |苏圣 2015-10-29 Efficient_mini-batch_training_for_stochastic_optimization ]]
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*[[媒体文件:2015_Fitnets-Hints for thin deep nets.pdf |张之勇 2015-10-29 2015_Fitnets-Hints for thin deep nets ]]
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*http://www.cs.cmu.edu/~muli/file/minibatch_sgd.pdf

2015年10月29日 (四) 07:14的最后版本


Papers To Read

  • 1, Learned-Norm pooling for deep feedforward and recurrent neural networks


Task schedules

Summary

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    Priority | Tasks name                    |      Status          |     Notions
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        1    | Bi-Softmax                    | ■■■□□□□□□□ | 1400h am training and problem fixing
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        2    | RNN+DAE                       | □□□□□□□□□□ |
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Speech Recognition

Multi-lingual Am training

Bi-Softmax

  • Using two distinct softmax for English and Chinese data.
  • Testing on 100h-Ch+100h-En, better performance observed.
  • Now testing the source code on 1400h_8k data, but stange decoding results got.Need to further investigate.

Reading Lists

取自“http://cslt.org/mediawiki/index.php?title=Zhiyong_Zhang&oldid=17373”