2014-03-21

Resoruce Building

• Current text resource has been re-arranged and listed

Leftover questions

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

AM development

Sparse DNN

• GA-based block sparsity

• code ready, testing on pure matrix multiplication

GMM - DNN co-training

• Co-training using Tencent data

• slightly better in GMM modeling when using DNN alignment
• worse performance when using the re-trained GMMs

Noise training

• Single noise injection

• White noise training
• Cafe noise training
• car noise training

• Multi noise injection

• white+cafe noise training

AMR compression re-training

• 1700h AMR training on going

GFbank

• gfbank is better than gfcc
• gfbank is better than fbank
• gfbank + fbank seems outperforms others

Word to Vector

• Data preparation

• Prepared 7 category totally 500+ articles
• Prepared Sogou 9-class text, totally 9*2000 articles
• Achieved Fudan 11-class text data, only for testing

• Improved wordvector with multi sense

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

• WordVecteor-based keyword extraction

• Decide to use the Sogou data to do extraction
• Evaluate the keyword in the classification task

• Wordvector based on classification

• Decide to use the Sogou data to do extraction

LM development

NN LM

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

• boundary-involved char NNLM training done
• Test on going

• Investigate MS RNN LM training

Pronunciation scoring

• G-score done on 16k English model
• The distribution of frames over phone/frame posterior scores seem highly discriminative
• The distribution of the distance of the test utterance against the reference utterance seems a high discriminative score

QA

FST-based matching

• Code done. Simple test done
• Ready for large scale test

Speech QA

• Class LM QA

• Now find that with smaller weight to the class FST, better performance is obtained
• Now it is very difficult to retrieve the words that can not be found by the original FST
• Test negative weights