2014-03-07

Resoruce Building

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AM development

Sparse DNN

• Optimal Brain Damage(OBD).

1. GA-based block sparsity

Efficient DNN training

1. Asymmetric window: Great improvement on training set(WER 34% to 24%), however the improvement is lost on test. Overfitting?

Multi GPU training

• Error encountered

GMM - DNN co-training

• Error encountered

Multilanguage training

1. Pure Chinese training reached 4.9%
2. Chinese + English reduced to 7.9%
3. English phone set should discriminate beginning phone and ending phone
4. Should set up multilingual network structure which shares low layers but separate languages at high layers

Noise training

• Train with wsj database by corrupting data with various noise types

• White noise + car noise training partially completed
• Mixture training produces better performance for both car and white noise
• Unknown noise testing is on progress

AMR compression re-training

• WeChat uses AMR compression method, which requires adaptation for our AM
• Test AMR & non-AMR model

               test-wav       WAV     AMR
        model
        WAV                   4.31     26.09
        AMR                  13.80      6.77 

• Prepare to do adaptation

GFbank

• Finished the first round of gfbank training & test
• The same gmm model (mfcc feature) was used to get the alignment
• Traing fbank & gfbank based on the mfcc alignment
• Clean training and noise test

     clean     25db    5db
gf   4.22      5.60    73.03
fb             5.87    84.12

Engine optimization

• Investigating LOUDS FST.

Word to Vector

• Test a training toolkit Standford University, which can involve global information into word2vector training

• C++ implementation (instead of python) for data pre-processing. Failed. Just use python.

• Basic wordvector plus global sense

• 1 MB corpus costs 5 mins,vocab size 16698
• 10 MB corpus costs about 82 mins vocab size 56287

• Improved wordvector with multi sense

• Almost impossible with the toolkit
• Can think of pre-training vectors and then do clusering

• WordVecteor-based keyword extraction

• Prepared 7 category totally 500+ articles
• A problem in keyword identification. Fix it by using the article vector space

• Investigating Senna toolkit from NEC. Intending to implement POS tagging based on word vectors.

LM development

NN LM

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

• Performance lower than word-based NNLM
• Prepare to run boundary-involved char NNLM

• WordVector-based word and char NNLM training done

• Google wordvecotr-based NNLM is worse than random initialized NNLM

3T Sogou LM

• Improved training

• 3T LM + Tencent 80k lM: performance worse than the original 80K LM
• Need to check if it is caused by the mismatched vocabu9lary
• 3T LM + QA LM : use online1 as the EM target, performance worse than QA LM
• Probably due to the incorrect EM target

QA Matching

• Working on edit FST for fuzzy matching
• TF/IDF score matching completed

Embedded development

• CLG embedded decoder is almost done. Online compiler is on progress.
• English scoring is under go

Speech QA

• N-best with entity LM was analyzed
• Entity-class LM comparision

• re-segmentation & re-train
• SRILM class-based LM ???
• Subgraph integration from Zhiyong

• WER summary is done
• Prepare to compose a paper