2014-01-10

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. Asymmetric window: Great improvement on training set(WER 34% to 24%), however the improvement is lost on test. Overfitting?
2. Fbank feature used to train GMM+DNN, leads to very high training Acc, but reduces accuracy on test.
3. Fbank energy trim. With threshold=10, we can get performance gain in a safe way. Larger thresholds may lead to performance reduction.here

Optimal phoneset

• Ch/En training with concatenated phone set is completed.
• Errors in testing.

Engine optimization

• Investigating LOUDS FST. On progress.

LM development

NN LM

• Working on NN LM based on word2vector.
• Reading more materials 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

• Work on layer-by-layer DNN training

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.
• Investigate some errors in entity-based LM.