“2014-02-14”版本间的差异

2014年2月14日 (五) 02:32的版本

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?

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

Engine optimization

• Decoder RT reached lower than 0.2: HCLG + MKL + icc
• Investigating LOUDS FST.

Adaptation

• Using linear hidden transform reduce WER from 14% to 11%.

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
• Ready for training 100M data
• Ready for training word sense
• Investigating Senna toolkit from . Intending to implement POS tagging based on word vectors.

LM development

NN LM

• Word-based and character-based NNLM using google word2vector completed
• Character-based NNLM completed (6000 characters, 7gram)

3T Sogou LM

• split the data into 24 sub sets, train 3gram for each set, prune with 1e-9
• Merge completed with equal weights

Embedded development

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

Speech QA

• Use N-best to expand match in QA. Better performance were obtained.

• 1-best matches 96/121
• 10-best matches 102/121

• Use N-best to recover errors in entity check.

• Design a non-entity pattern to discover the possible place of an entity
• By this position range, search entities within the N-best result

• Use Pinyin to recover errors in entity check. Future work.

• Design a non-entity pattern to discover the possible place of an entity (as above)
• Match the Pinying strings of all the entities, and then match the pinyin strings with the entity pinyin
• Keep the most matched entity based on Pinyin with a threshold
• A bit worse then the original test.
• A possible problem is that the LM is over-strong, thus lead to unmatched Pinyin string in acoustic space
• Liu rong will provide a weak LM to support the research.

• Investigate some errors in entity-based LM.

• Still some errors exist
• Running entity-base LM with a small entity list