2013-05-17

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Data sharing

  • LM count files still undelivered!

DNN progress

Experiments

  • setups for mfcc/plp
Test Set fMMI s1/tri1 s2/tri1 s3/tri1 s4/tri1 s2/tri2 s4/tri2 cpu-based (like s4/tri1) plp-s2/tri2
map 28.58 25.38 24.47 26.16 26.20 22.85 24.27 26.45 23.86
2044 24.79 23.58 22.82 23.84 24.13 21.45 22.76 24.66 22.68
notetp3 21.64 16.08 14.89 15.92 15.97 14.89 14.79 16.14 16.46
1900 8.19 8.55 8.43 8.66 8.90 7.30 7.91 8.23 7.68
general 39.63 36.18 34.79 35.88 35.90 33.06 33.79 38.02 34.12
online1 35.19 34.68 33.90 33.45 33.38 32.93 32.43 33.00 33.60
online2 28.30 27.27 26.61 26.26 26.36 25.94 25.69 26.63 26.20
speedup 28.45 24.97 24.40 24.55 25.42 23.04 23.67 27.17 23.62


Tencent exps

  1. 手动将NN网络的W权重,较小的置零,在保留30%左右的较大权重的条件下,系统性能未见明显衰减(how much in number? it would be interesting to re-train the net after pruning the weights)。
  2. 按照HTK模型的结构,以及HTK align的结构,修改Kaldi的GPU接口,验证并无问题,已开始较大规模数据训练(1000小时),网络结构前后5帧扩展,4个隐层,每层2048节点,输出15000个状态。使用mpe模型alignment,特征为plp特征,未做任何映射。
  3. 解码器仍在CLG结构下,修改声学模型计算接口,接入DNN模型,验证无问题,已开始效率优化。

待做实验:

  1. 验证不同学习率调节策略,指数下降衰减方式,newbob方式。
  2. 验证不同特征在大数据上的作用。
  3. 最后层不过softmax,降维得到BN特征实验,类似IBM BN做法 (if this is a linear dim-reduction, it might be worse than the BN...)

GPU & CPU merge

  1. just started


Kaldi/HTK merge

  • HTK2Kaldi: hold.
  • Kaldi2HTK: pdf error problem.
Kaldi Monophone: 30.91%  HDecode: 41.40%
  • workaround; use the BN feature to train HTK models, so without kaldi training.

Embedded progress

  • Status:
  1. first embedded demo done, 1000 words take 3.2M memory.
  2. accuracy test finished. The test data involves 3 speakers recorded in a car with Chongqing dialect, 1000 address names.
  3. training acoustic model for sphinx. The an4 training process is done, while the test seems problematic.
Test Set #utt ERR RT
806 23.33 0.07
887 13.64 0.08
876 17.58 0.07
  • To be done
  1. finish the large scale AM training