ASR:2015-03-23

Speech Processing

AM development

Environment

• grid-11 often shut down automatically, too slow computation speed.
• GPU has being repired.--Xuewei

RNN AM

• details at http://liuc.cslt.org/pages/rnnam.html
• tuning parameters on monophone NN

Mic-Array

• reproduce environment for interspeech
• investigate alpha parameter in Lasso

Dropout & Maxout & rectifier

• HOLD
• Need to solve the too small learning-rate problem
• 20h small scale sparse dnn with rectifier. --Mengyuan
• 20h small scale sparse dnn with Maxout/rectifier based on weight-magnitude-pruning. --Mengyuan Zhao

Convolutive network

• HOLD

• CNN + DNN feature fusion
• reproduce experiments -- Yiye

RNN-DAE(Deep based Auto-Encode-RNN)

• HOLD -Zhiyong
• http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=261

Speech rate training

• http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=268
• Technical report HOLD.-- Xiangyu Zeng, Shi Yin
• Paper for NCMMSC done

Neural network visulization

• http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=324
• Technical report done --Mian Wang.

Speaker ID

• DNN-based sid --Yiye
• http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=327

Ivector based ASR

• http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?step=view_request&cvssid=340
• Ivector dimention is smaller, performance is better
• Augument to hidden layer is better than input layer

Text Processing

LM development

Domain specific LM

• LM2.X

• train a large lm using 25w-dict.(hanzhenglong/wxx)

• v2.0c filter the useless word.(next week)
• set the test set for new word (hold)

• prepare the wiki data: entity list.

tag LM

• Tag Lm(JT)

• error check

• similar word extension in FST

• repeat the experiment using same data

RNN LM

• rnn

• the input and output is word embedding and add some token information like NER..
• map the word to character and train the lm.

• lstm+rnn

• check the lstm-rnnlm code about how to Initialize and update learning rate.(hold)

Word2Vector

W2V based doc classification

• data prepare.(hold)

Knowledge vector

• make a report on Monday

Translation

• v5.0 demo released

• cut the dict and use new segment-tool

Sparse NN in NLP

• prepare the ACL

• check the code to find the problem .
• increase the dimension
• use different test set.

online learning

• data is ready.prepare the ACL paper

• prepare sougouQ data and test it using current online learning method

framework

• extract the module

• extract the context module ,search module,entity recognize module and common module.
• define the inference in different modules

• composite module

leftover problem

• new inter will install SEMPRE