2013-06-07

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

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

Tencent exps

• 6000h model training, could be finished on 25th approximately.
• working on sequential DNN DT: refer to "Error Back Propagation For Sequence Training of Context-Dependent Deep Networks For Conversation Speech Transcription"

GPU & CPU merge

1. on progress.

RNN LM progress

• Initial work started. 100M data with a 10k vocabulary obtained a perplexity 180.
• More exploration continuous.

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

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