“ASR:2015-05-18”版本间的差异
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（→relation classifier） |
（→binary vector） |
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| （相同用户的3个中间修订版本未显示） | |||
| 第65行： | 第65行： | ||
* CNN adapt to resolve the low resource problem | * CNN adapt to resolve the low resource problem | ||
===Translation=== | ===Translation=== | ||
| − | * similar-pair method in | + | *Test the performance of the similar-pair method in bilingual recognition |
| − | + | ||
| − | + | ||
===Order representation === | ===Order representation === | ||
* modify the objective function | * modify the objective function | ||
* sup-sampling method to solve the low frequence word | * sup-sampling method to solve the low frequence word | ||
| + | * Sort out vectors and do the experiment on objective function convergence | ||
| + | * test on classification task and prediction task | ||
| + | |||
===binary vector=== | ===binary vector=== | ||
| + | *Finish hamming metric binary vector. | ||
| + | *Try to finish binary vector. | ||
| + | *Do test report. | ||
===Stochastic ListNet=== | ===Stochastic ListNet=== | ||
| − | * | + | *To finish writing first edition of emnlp 2015 long paper |
===relation classifier=== | ===relation classifier=== | ||
2015年5月25日 (一) 00:59的最后版本
Speech Processing
AM development
Environment
- grid-15 often does not work
- grid-14 often does not work
RNN AM
- details at http://liuc.cslt.org/pages/rnnam.html
- Test monophone on RNN using dark-knowledge --Chao Liu
- run using wsj,MPE --Chao Liu
- run bi-directon --Chao Liu
- train RNN with dark knowledge transfer on AURORA4 --zhiyuan
Mic-Array
- hold
- Change the prediction from fbank to spectrum features
- investigate alpha parameter in time domian and frquency domain
- ALPHA>=0, using data generated by reverber toolkit
- consider theta
- compute EER with kaldi
RNN-DAE(Deep based Auto-Encode-RNN)
- deliver to mengyuan
Speaker ID
- DNN-based sid --Yiye Lin
Ivector&Dvector based ASR
- hold --Tian Lan
- Cluster the speakers to speaker-classes, then using the distance or the posterior-probability as the metric
- Direct using the dark-knowledge strategy to do the ivector training.
- Ivector dimention is smaller, performance is better
- Augument to hidden layer is better than input layer
- train on wsj(testbase dev93+evl92)
Dark knowledge
- Ensemble using 100h dataset to construct diffrernt structures -- Mengyuan
- adaptation English and Chinglish
- Try to improve the chinglish performance extremly
- unsupervised training with wsj contributes to aurora4 model --Xiangyu Zeng
- test large database with AMIDA
- test hidden layer knowledge transfer--xuewei
bilingual recognition
- hold
- http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=359 --Zhiyuan Tang and Mengyuan
language vector
- train DNN with language vector--xuewei
Text Processing
RNN LM
- character-lm rnn(hold)
- lstm+rnn
- check the lstm-rnnlm code about how to Initialize and update learning rate.(hold)
W2V based document classification
- make a technical report about document classification using CNN --yiqiao
- CNN adapt to resolve the low resource problem
Translation
- Test the performance of the similar-pair method in bilingual recognition
Order representation
- modify the objective function
- sup-sampling method to solve the low frequence word
- Sort out vectors and do the experiment on objective function convergence
- test on classification task and prediction task
binary vector
- Finish hamming metric binary vector.
- Try to finish binary vector.
- Do test report.
Stochastic ListNet
- To finish writing first edition of emnlp 2015 long paper
relation classifier
- Tune the best model.
- Train on new wordembedding.
- Do some analysis(length of context, track the pooling.)
- Finish the draft.
plan to do
- combine LDA with neural network
