2014年4月4日 (五) 01:59 Cslt

2014-04-04T01:59:58Z

Cslt：以内容“==Resoruce Building== * Current text resource has been re-arranged and listed == Leftover questions== * Asymmetric window: Great improvement on training set(WER 34% to...”创建新页面

2014-04-04T01:58:10Z

以内容“==Resoruce Building== * Current text resource has been re-arranged and listed == Leftover questions== * Asymmetric window: Great improvement on training set(WER 34% to...”创建新页面

新页面

==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.
* 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 experiments with no-noise
:* More experiments with additional noise types

===AMR compression re-training===

* 1700h MPE adaptation

:* iter1:
amr: %WER 13.40 [ 6398 / 47753, 252 ins, 829 del, 5317 sub ]
wav: %WER 11.19 [ 5343 / 47753, 178 ins, 710 del, 4455 sub ]

:* iter2:
amr: %WER 13.31 [ 6358 / 47753, 255 ins, 798 del, 5305 sub ]
wav: %WER 11.33 [ 5409 / 47753, 180 ins, 732 del, 4497 sub ]

:* iter3:
amr: %WER 13.25 [ 6326 / 47753, 230 ins, 823 del, 5273 sub ]
wav: %WER 11.43 [ 5460 / 47753, 199 ins, 709 del, 4552 sub ]

:* iter4:
amr: %WER 13.17 [ 6289 / 47753, 225 ins, 833 del, 5231 sub ]
wav: %WER 11.44 [ 5461 / 47753, 200 ins, 693 del, 4568 sub ]

:* iter5:
amr: %WER 13.17 [ 6291 / 47753, 254 ins, 769 del, 5268 sub ]
wav: %WER 11.46 [ 5471 / 47753, 200 ins, 696 del, 4575 sub ]

===GFbank===
* Found errors attributed to speaker2utterance

===Denoising & Farfield ASR====
* First round of recording failed
* Record farfield wave in next week

===VAD===
* Source code prepared
* Prepare DNN pipeline

==Word to Vector==

* LDA baseline (sogou 1700*9 training set)
:* Training done
:* Training classifier

==LM development==

===NN LM===

* Character-based NNLM (6700 chars, 7gram), 500M data training done.
:* boundary-involved char NNLM training done
:* Word-boundary seems less important than char history

* Investigate MS RNN LM training

==Pronunciation scoring==
* 8k model delivered
* MLP-based scoring completed

==QA==

===FST-based matching===
:* Char FST on investigation
:* FST-based QA patent done

===Speech QA===
*Class LM QA
:* excellent done
:* investigated various stepping-in weights, found negative weights (-1) is effective for encourage entity recognition
:* investigated performance reduction due to the preference on small words. Introduced a factor on L.fst to discourage short words.

@@ 第49行： / 第49行： @@
 * Found errors attributed to speaker2utterance
-===Denoising & Farfield ASR====
+===Denoising & Farfield ASR===
 * First round of recording failed
 * Record farfield wave in next week

2014-04-04 - 版本历史

2014年4月4日 (五) 01:59 Cslt

Cslt：以内容“==Resoruce Building== * Current text resource has been re-arranged and listed == Leftover questions== * Asymmetric window: Great improvement on training set(WER 34% to...”创建新页面