2013-08-10

Data sharing

• LM count files still undelivered!

DNN progress

Discriminative DNN

• Running 1200-3620 NN, graph generation is done. DT training should be done in 3 days.

Sparse DNN

• Iterative sparse sticky training runs. More sparsity is expected.

Tencent exps

• online support
• garbage model training
• VAD optimization

DNN Confidence estimation

• Distribution graph is obtained. The performance seems bad.
• A possible reason is that the decoding is LM-based, and the confidence is only acoustic related. So (1) the errors in linguistic layer are not really errors in the acoustic layer (2) the search will automatically choose the almost-correct phones/states.
• The conclusion is that the DNN confidence is most suitable for grammar-based applications, or at least LM information is not very important.

• To be done:

1. CI phone confidence, on going
2. No-tone confidence, on going

GFCC DNN

• GFCC computing is highly slow. 100 hour speech costs 16 hour cpu time. RT is around 0.2. It is intolerable.
• GFCC-based DNN training for 100 hour speech data is done. Need to test the noise-robust performance in 2 days.

Stream decoding

• the code is done. Simple testing is completed.
• Problem 1: CMN initialization is not perfect. Need to train a better initial CMN model.
• Problem 2: balance for posterior-based silence detection.

Subgraph integration

• G.fst integration is done. Initial test passed. Looks like the zero-probability is better for the NUM class.
• HCLG integration is done. A bug fixed, passed initial test.
• Online integration cost is 1 minute. Need to optimize.
• Need thorough testing with the Tencent test suite.
• Need to tune the subgraph feeding probability.

Embedded progress

• GFCC-based engine test
• Attain a performance curve: RT,memory size,package size Vs vocabulary size.
• A new demo released for 4600 song names.