2014-01-03

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AM development

Sparse DNN

  • Optimal Brain Damage(OBD).
  1. Online OBD held.
  2. OBD + L1 norm start to investigation.
  • Efficient computing
  1. Conducting rearrangement the matrix structure and compose zero blocks by some smart approaches, leading to better computing speed.


Efficient DNN training

  1. L1-L2 grid checking: L1/L2(< 1e-6) seems good for record1900 but worse for other test sets.

link here

  1. Asymmetric window: Great improvement on training set(WER 34% to 24%), however the improvement is lost on test. Overfitting?
  2. Frame-skipping. Skipping 1 frame speeds up decoding in a consistent way while retaining the accuracy largely. Skipping more frames lead to unacceptable performance degradation.
  3. Interpolation does not provide performance gain.

link here

Optimal phoneset

  • Analyze Tencent English phone set. Found some errors in CH/EN phone sharing.
  • Develop a new sharing scheme, start training the new system.
  • Start training for all-separated phones
  • Start training mixed system with Chinglish data.


Engine optimization

  • Investigating LOUDS FST. On progress.


LM development

NN LM

  • Collecting a bigger lexicon: 40k words related to music, 56k words from an official dictionary.
  • Working on NN LM based on word2vector.

Embedded development

  • Liuchao's cellphone, Qualcomm Snapdragon Krait MSM8960 @ 1.5GHz, using 1 core

small nnet 100/600/600/600/600/1264 with MFCC input

  • 4500 words:
  • construct LG: 0.41s
  • compose HCLG with det: 13.70s, 5.318 MB
  • compose HCLG without det: 6.61s, 5.488 MB
  • 950 words:
  • construct LG: 0.15s
  • compose HCLG with det: 2.63s, 0.947 MB, decode RT 0.649
  • compose HCLG without det: 1.74s, 0.998 MB, decode RT 0.548
  • For word list or simple grammars, determinization leads to small RT increase, but can improve HCLG compiling dramatically. This is particularly the case for embedded devices.
  • The accuracy does not change with/without determinization.


Speech QA

  • Use N-best to expand match in QA. Better performance were obtained.
  • 1-best matches 96/121
  • 10-best matches 102/121
  • Use N-best to recover errors in entity check. Working on.
  • Use Pinyin to recover errors in entity check. Future work.