“2013-06-07”版本间的差异

2013年6月7日 (五) 07:42的版本

Data sharing

LM count files still undelivered!

DNN progress

Experiments

sparse DNN: sticky training (retrain the nnet while keeping the sparsness)

zero small values(test set: 1900), with extremely sparseness:

threshold	0	0.2	0.3	0.4	0.5
shrinkage%	0.0	66.4	81.6	0.90	0.94
without sticky: WER	7.55	9.46	53.23	98.99	-
with sticky: WER	7.55	7.56	7.87	8.81	9.87

Conclusion: The extremely sparse network can largely pertain the performance of DNN. The structure seems more important than the parameter tuning based on the structure.

fixed-point DNN forwarding

working on migrating the Atlas lib to ARM.
working on atlas/mkl independent implementation.

Tencent exps

6000小时模型训

GPU & CPU merge

on progress.

Embedded progress

Status:

check the reference, and change the compiling options

the large-scale AM training based on the Tencent 400h data is done, continuous HMM.

Sys	WER	RT
SP model	8.81	0.07
Tencent tone	6.33	0.40
Tencent notone	5.04	0.31

To be done

large scale parallel training.
NN based engine(dynamic and static).
Semi-continuous model with the Tencent data

@@ 第47行： / 第47行： @@
 ! Sys !! WER !! RT
 |-
-| PS model       || 8.81  || 0.07
+| SP model       || 8.81  || 0.07
 |-
 | Tencent tone   || 6.33  || 0.40