2013-06-28

Data sharing

• LM count files still undelivered!

DNN progress

Experiments

• Sparse DNN.

1. With Atlas without any change, on the ARM platform, obtained RT 2.0. Will change the Atlas code to support sparse matrices.

GPU & CPU merge

1. Hold

RNN LM progress

• Use 100M text, 10k lexicon in training. Validation test set is obtained from the transcription of the Tencent online1 speech data.

100 hidden layer, 1 hidden layer, 3-gram

training time: 7 hour, 8GB

prediction time: quick, 8GB

3-gram PPL: 227.323 WER: 36%

RNN PPL: 170.056129 WER: 41%

3-gram+RNN: PPL: 0.25RNN+0.75 3-gram: 180.0 WER 35%

• possibly a bug when computing PPL with the RNN toolkit.

Embedded progress

• Status:

1000 test words + 2000 noise words

        before  |  after 
 utt    952        3317
%wer    6.26%     11.04%
 RT     0.07      0.20

This means the GMM-based system highly relies on the vocabulary. It may work well with small lexica, but difficult with large ones.

Run other optimization parameters:

 option   |   RT    |   %wer

 original    0.07       6.28

  -ds        0.06       6.33%
  -topn      0.06       6.80%  
  -maxwpf     -          -
  -maxhmmpf   -          - 
  -kdmaxdepth -          -
  -kdmaxbbi   -          -
  -pl_window  -          -

• To be done

1. sparse DNN based Kaldi engine
2. sparse DNN based PS engine