2014-07-05

Leftover questions

• Asymmetric window: Great improvement on training set(WER 34% to 24%), however the improvement is lost on test.
• Multi GPU training: Error encountered
• Multilanguage training
• Investigating LOUDS FST.
• CLG embedded decoder plus online compiler.
• DNN-GMM co-training

AM development

Sparse DNN

• GA-based block sparsity (+++++++++)

Noise training

• Journal paper writing on going

Multilingual ASR

                                   HW 27h (HW TR LM not involved)     HW27h (HW TR LM involved)
Fbank stream (monolang)             21.64                                   20.72
FBank non-stream (MPE4)             22.23                                   21.38
FBank stream (MPE4)                 21.99                                     -  

Denoising & Farfield ASR

• Reverberant data delivered
• global CMN based spectrum checking done. Seems the signal/feature transform with DNN is not a very reasonable waycheck here.

VAD

• Waiting for engineering work

Scoring

• Refine the acoustic model with AMIDA database. problem solved by involving both wsj and AMIDA.

Embedded decoder

• WER vs RT vs graph size done.
• The first deliver is Emb201407_BG_v0.0
• Demo done

LM development

Domain specific LM

h2. Domain specific LM construction

h3. Mixture LM

• TAG model: 127h HuaWei tag analysis done.
• Performance on the NUM-tagged model under testing.

Word2Vector

W2V based doc classification

• Good performance obtained with the SSA (semantic space allocation). That is, train a general GMM, and then represent each doc as the vector of the GMM weight.
• APSIPA paper submitted

Semantic word tree

• Version v2.0 released (filter with query log)
• Please deliver to /nfs/disk/perm/data/corpora/semanticTree (Xingchao)
• Version v3.0 under going. Further refinement with Baidu Baike hierarchy

NN LM

• Character-based NNLM (6700 chars, 7gram), 500M data training done.

• Inconsistent pattern in WER were found on Tenent test sets
• probably need to use another test set to do investigation.

• Investigate MS RNN LM training