2014年9月22日 (一) 02:24 Cslt

2014-09-22T02:24:17Z

Cslt：/* QA */

2014-09-22T02:16:23Z

‎QA

Cslt：以“==Resoruce Building== == Leftover questions== * Investigating LOUDS FST. * CLG embedded decoder plus online compiler. * DNN-GMM co-training * NN LM == AM develop...”为内容创建页面

2014-09-22T02:16:06Z

以“==Resoruce Building== == Leftover questions== * Investigating LOUDS FST. * CLG embedded decoder plus online compiler. * DNN-GMM co-training * NN LM == AM develop...”为内容创建页面

新页面

==Resoruce Building==

== Leftover questions==

* Investigating LOUDS FST.
* CLG embedded decoder plus online compiler.
* DNN-GMM co-training
* NN LM

== AM development ==

=== Sparse DNN ===
* Investigating layer-based DNN training

===Noise training===
:* First draft of the noisy training journal paper
:* Check abnormal behavior with large sigma (Yinshi, Liuchao)

==Drop out & Rectification & convolutive network==

* Drop out

:* No performance improvement found yet.
:* [http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=wangd&step=view_request&cvssid=261]

* Rectification
:* Dropout NA problem was caused by large magnitude of weights

* Convolutive network
# Test more configurations

===Denoising & Farfield ASR===

* Lasso-based de-reverberation is done with the REVERBERATION toolkit
:* Start to compose the experiment section for the SL paper.

===VAD===

* Noise model training done. Under testing.
* Need to investigate the performance reduction in babble noise. Call Jia.

===Speech rate training===

* Some interesting results with the simple speech rate change algorithm was obtained on the WSJ db
[http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=wangd&step=view_request&cvssid=268]

* Seems ROS model is superior to the normal one with faster speech
* Need to check distribution of ROS on WSJ
* Suggest to extract speech data of different ROS, construct a new test set
* Suggest to use Tencent training data
* Suggest to remove silence when compute ROS

===Scoring===

* Pitch & rythmn done.
* Harmonics hold

===Confidence===

* Basic confidence by using lattice-based posterior + DNN posterior + ROS done
* 23% detection error achieved by balanced model

==LM development==

===Domain specific LM===

h2. domain specific count dumped
h2. ngram generation is on going

h2. NUM tag LM:

* HCLG union seems better than G union, when integrating grammar + LM (25->23)
* Boost specific words like wifi if TAG model does not work for a particular word.

==Word2Vector==

===W2V based doc classification===

* Initial results variable Bayesian GMM obtained. Performance is not as good as the conventional GMM.
* Non-linear inter-language transform: English-Spanish-Czch: wv model training done, transform model on investigation
:* probably over-fitting with the MLP training
:* SSA-based local linear mapping still on running

* Knowledge vector started
:* document obtained from wiki

* Character to word conversion
:* Design the transform model

==RNN LM==

* Prepare WSJ database
* Trained model 10000 x 4 + 320 + 10000
* Better performance obtained (4.16-3.47)
* gigaword sampling for Chinese data

==Speaker ID==

* Second model done

==Emotion detection==

* delivered to Sinovoice

==Translation==

* v3.0 demo released

==QA==

* Framework done

@@ 第114行： / 第114行： @@
 * v3.0 demo released
 ==QA==
-* Framework design done
+* Huilan framework design done

←上一版本		2014年9月22日 (一) 02:16的版本
第117行：		第117行：
	==QA==		==QA==

−	* Framework done	+	* Framework design done

2014-09-22 - 版本历史

2014年9月22日 (一) 02:24 Cslt

Cslt：/* QA */

Cslt：以“==Resoruce Building== == Leftover questions== * Investigating LOUDS FST. * CLG embedded decoder plus online compiler. * DNN-GMM co-training * NN LM == AM develop...”为内容创建页面