2014-05-16

Resoruce Building

• Maxi onboard
• Release management should be started: Zhiyong (+)
• Blaster 0.1 & vivian 0.0 system release

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.
• DNN-GMM co-training

AM development

Sparse DNN

• GA-based block sparsity (+++)

• Found a paper in 2000 with similar ideas.
• Try to get a student working on high performance computing to do the optimization

Noise training

• More with-clean training completed. 2 conditions left

GFbank

• 8k train

• GFBank sinovoice 1400 MPE stream

• 16k train

• GFBank sinovoice 6000 MPE1 stream: worse than 1700h (10.18-11.11)

Multilingual ASR

• Test sharing scheme:

• decision tree share, xent improvement obtained, MPE no improvement (Chinese worse a bit, English a bit better).

English model

                             mic          tel
pure eng                    voxforge    fisher
chinese eng                 shujutang   convert-from-shujutang 

Denoising & Farfield ASR

• Baseline: close-talk model decode far-field speech: 92.65
• Will investigate DAE model.

Kaiser Window

window function test based on 23 Mel channel number  8k wsj databas	
window function	%WER	       ins	del     sub
kaiser	 278 / 5643=4.93	39	15	224
povey	 265 / 5643=4.70	34	14	217

window function test based on 30 Mel channel number  8k wsj databas
window function	%WER	        ins	del	sub
kaiser	 270 / 5643= 4.78	38	17	215
povey	283 / 5643= 5.02 	36	24	223

VAD

• DNN-based VAD (24.77) shower better performance than energy based VAD (45.73)

Scoring

• online scoring done??
• checked into gitlab?

Word to Vector

• Paper submitted

LM development

Domain specific LM

• Prepare English lexicon

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

QA

FST-based matching

• Word-based FST 1-2 seconds with 1600 patterns. Huilan's implementation <1 second.
• THRAX toolkit for grammar to FST

• Investigate determinization of G embedding

• Refer to Kaldi new code