Cslt：/* Embedded decoder */

2014-06-13T06:53:14Z

‎Embedded decoder

Cslt：以内容“==Resoruce Building== * Release management has been started == Leftover questions== * Asymmetric window: Great improvement on training set(WER 34% to 24%), however the...”创建新页面

2014-06-13T06:38:44Z

以内容“==Resoruce Building== * Release management has been started == Leftover questions== * Asymmetric window: Great improvement on training set(WER 34% to 24%), however the...”创建新页面

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==Resoruce Building==
* Release management has been started

== Leftover questions==
* Asymmetric window: Great improvement on training set(WER 34% to 24%), however the improvement is lost on test.
* 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 (++++++)
* Paper revision under going.

===Noise training===
:* Paper writing will be started this week

===GFbank===

* Running into Sinovoice 8k 1400 + 100 mixture training.
* GFbank 14 xEnt iteration completed:
Huawei disanpi BJ mobile 8k English data
FBank non-stream (17 iteration) 22.01% 26.63% 33.83%
FBank non-stream (MPE1) 21.07% 22.91% 24.34%
GFbank stream (18 iteration) 22.26%; 27.79% 35.10%
GFbank non-stream (16 iteration) 22.45%; 27.25% 34.64%

===Multilingual ASR===

* TAG-based modeling is ok with a smaller step-in factor.
* Non-tag test should be conducted on both Baidu & micro blob data

===Denoising & Farfield ASR===

* With artificial reverberant, 2 x 1200 seems a more appropriate configuration. However great randomness was seen.
* With utterance-cmn, performance seems better than no-cmn, however, with global-cmn, the performance was strangely reduced.
* Should experiment with single-layer network with more hidden units.
* Record another set of far-field database. Pre-process done.
* Need obtain a baseline result with the new middle-far mic.

===VAD===

* DNN-based VAD (7.49) showers much better performance than energy based VAD (45.74)
* Need to test small scale network (+)
:* 600-800 network test (+)
:* 100 X 4 + 2 network training (+)

===Scoring===

* Collect more data with human scoring to train discriminative models

===Embedded decoder===

600X4+800 AM, beam9:

150k 20k 10k 5k
WER 15.96 - - -
RT X 0.94 - -

==LM development==

===Domain specific LM===

* Cross entropy filtering is not better than the key-based filtering.
* Seems possible to reduce the PPL with the extra retrieved data source.

==Word2Vector==

* Design network spider
* Design semantic related word tree
:* First version based on pattern match done
:* Filter with query log
:* Further refinement with Baidu Baike hierarchy

===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

@@ 第60行： / 第60行： @@
 ===Embedded decoder===
-X4+800 AM, beam9:
+<pre>
 k       20k     10k      5k
 WER     15.96       -       -       -
 RT       X         0.94     -       -
+</pre>
 ==LM development==

2014-06-13 - 版本历史

Cslt：/* Embedded decoder */

Cslt：以内容“==Resoruce Building== * Release management has been started == Leftover questions== * Asymmetric window: Great improvement on training set(WER 34% to 24%), however the...”创建新页面