“Zhiyong Zhang”版本间的差异

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Summary
第5行: 第5行:
 
     Priority | Tasks name                    |      Status          |    Notions
 
     Priority | Tasks name                    |      Status          |    Notions
 
     --------------------------------------------------------------------------------------------------------     
 
     --------------------------------------------------------------------------------------------------------     
         1    | Bi-Softmax                    | ■■■■■■■□□□ | 1400h am training and problem fixing
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         1    | Bi-Softmax                    | ■■■□□□□□□□ | 1400h am training and problem fixing
 
     --------------------------------------------------------------------------------------------------------
 
     --------------------------------------------------------------------------------------------------------
 
         2    | RNN+DAE                      | □□□□□□□□□□ |
 
         2    | RNN+DAE                      | □□□□□□□□□□ |

2015年1月10日 (六) 01:50的版本

Task schedules

Summary

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

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.

Papers To Read

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