2014年6月3日 (二) 02:28 Cslt

2014-06-03T02:28:33Z

2014年6月3日 (二) 02:28 Cslt

2014-06-03T02:28:15Z

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

2014-06-03T02:09:57Z

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. 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 (+++++)

===Noise training===
:* All experiments completed.
:* Paper writing will be started this week

===GFbank===

* Test on Tencent database is done. Better performance observed than Fbank
* Equal-loudness pre-filter added, slightly better performance was obtained
* Running into Sinovoice 8k 1400 + 100 mixture training. 9 xEnt iteration completed.

===Multilingual ASR===

* Multilingual LM decoding
* Fixing the non-tag bug ???

===English model===

<pre>

(state-gauss = 10000 100000, various LM, beam 13)

1. Shujutang 100h chi-eng 16k:

LM/AM | xEnt | mpe_1 | mpe_2 | mpe_3 | mpe_4 |
--------- --------- --------- --------- --------- ---------
wsj | 23.86 | 20.95 | 20.90 | 20.84 | 20.81 |
cmu | 22.22 | - | - | - | 18.83 |
giga | 21.77 | - | - | - | 18.61 |
armid | 20.45 | - | - | - | - |

2. Shujutang 100H chi-eng 8k:

LM/AM | xEnt | mpe_1 | mpe_2 | mpe_3 | mpe_4 |
--------- --------- --------- --------- --------- ---------
wsj | 26.27 | 23.63 | 23.14 | 22.93 | 23.00 |
cmu | 24.11 | - | - | - | 20.36 |
giga | 23.11 | - | - | - | 20.11 |
armid | - | - | - | - | - |

3. voxforge pure eng 16k:

LM/AM | xEnt | mpe_1 | mpe_2 | mpe_3 | mpe_4 |
--------- --------- --------- --------- --------- ---------
wsj | 21.38 | 24.89 | 24.50 | 23.31 | 23.13 |
cmu | 24.00 | - | - | - | 21.33 |
giga | 18.75 | - | - | - | 22.45 |
armid | - | - | - | - | - |

4. fisher pure eng 8k:
Not finish yet.
LM/AM | xEnt | mpe_1 | mpe_2 | mpe_3 | mpe_4 |
--------- --------- --------- --------- --------- ---------
wsj | 40.65 | 36.16 | 35.94 | 35.88 | 35.80 |
cmu | 35.07 | - | - | - | 31.16 |
giga | 41.18 | - | - | - | 36.23 |
armid | - | - | - | - | - |
</pre>

===Denoising & Farfield ASR===

* Investigating DAE model
* Kaldi-based MSE obj training toolkit preparation

===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
:* 100 X 4 + 2

===Scoring===

* Bug for the stream mode fixed

===Embedded decoder===

* word list graph test passed
* wlist2LG toolkit checked in
* Prepare to deliver Android compiler options (.mk)
* Interface design should be completed in one day
* Prepare HCLG for 20k LM, decoding on progress.

==LM development==

===Domain specific LM===
* English lexicon done, build HCLG
* Re-build LM with the new lexicon
* Tested on Dianxin dev set

===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

←上一版本		2014年6月3日 (二) 02:28的版本
第120行：		第120行：

	* Investigate MS RNN LM training		* 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~~

@@ 第29行： / 第29行： @@
 * Multilingual LM decoding
-* Fixing the non-tag bug ???
+* non-tag bug investigation with some digital string recordings
 ===English model===
@@ 第106行： / 第108行： @@
 ===Domain specific LM===
 ===NN LM===

2014-06-03 - 版本历史

2014年6月3日 (二) 02:28 Cslt

2014年6月3日 (二) 02:28 Cslt

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