“2014-02-28”版本间的差异

2014年2月28日 (五) 02:24的最后版本

Resoruce Building

• Current text resource has been re-arranged and listed

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 training completed. All results are fine
• Car noise training almost finished. Large-variance training on progress

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

• wordvector keyword extraction seems more reasonable if the keywords are in the lexicon
• For oov words, wv-based extraction is limited by the vocabulary
• Need a standard new word extraction

• 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.

• 3hours per iteration
• For word-based NNLM, 1 hour/iteration for 1024 words, 4 hours/iteration for 10240 words
• Performance lower than word-based NNLM

• WordVector-based word and char NNLM training done

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

3T Sogou LM

• Improved training

• re-segmentation by Tencent 110k lexicon
• re-train with 4G text blocks
• 1/6 merge done. PPL reduced to 466(vs Tencent 8w8 213.74)
• Need to check the OOV problem
• Need to finish the final merge.

Embedded development

• CLG embedded decoder is almost done. Online compiler is on progress.
• Zhiyong is working on layer-by-layer DNN training.

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