“ASR:2015-12-7”版本间的差异
来自cslt Wiki
（以“==Speech Processing == === AM development === ==== Environment ==== ==== End-to-End ==== *monophone ASR --Zhiyuan :* MPE :* CTC/nnet3/Kaldi ==== conditioning lea...”为内容创建页面） |
（→language vector） |
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| （相同用户的2个中间修订版本未显示） | |||
| 第50行： | 第50行： | ||
* RNN language vector | * RNN language vector | ||
:*hold | :*hold | ||
| − | + | * language vector into multiple layers --Zhiyuan | |
| + | :* a Chinese paper | ||
| + | * speech rate into multiple layers --Zhiyuan | ||
| + | :*verify the code for extra input(s) into DNN | ||
===multi-GPU=== | ===multi-GPU=== | ||
| 第59行： | 第62行： | ||
:* nnet3 mpe code is under investigation | :* nnet3 mpe code is under investigation | ||
:*http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=472 | :*http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=472 | ||
| − | * | + | *AM training on big dataset --mengyuan |
| − | * | + | :* fix decode bug |
| − | * | + | :* nnet3 lstm & blstm training on sinovoice 120h dataset, using KALDI's default config. but result is worse than tdnn |
| − | * train mpe | + | * train mpe --Zhiyong,Xuewei |
| − | * train nnet3 mpe using data from Jietong--Xuewei | + | :*train nnet3 mpe using data from Jietong--Xuewei |
| − | + | :* modify code to print stats | |
| + | :*modify code to reduce memory | ||
| + | |||
===multi-task=== | ===multi-task=== | ||
* test according to selt-information neural structure learning --mengyuan | * test according to selt-information neural structure learning --mengyuan | ||
2015年12月20日 (日) 11:15的最后版本
目录
Speech Processing
AM development
Environment
End-to-End
- monophone ASR --Zhiyuan
- MPE
- CTC/nnet3/Kaldi
conditioning learning
- language vector into multiple layers --Zhiyuan
- a Chinese paper
- speech rate into multiple layers --Zhiyuan
- verify the code for extra input(s) into DNN
Adapative learning rate method
- sequence training -Xiangyu
- write a technique report
Mic-Array
- hold
- compute EER with kaldi
Data selection unsupervised learning
- hold
- acoustic feature based submodular using Pingan dataset --zhiyong
- write code to speed up --zhiyong
- curriculum learning --zhiyong
RNN-DAE(Deep based Auto-Encode-RNN)
- hold
- RNN-DAE has worse performance than DNN-DAE because training dataset is small
- extract real room impulse to generate WSJ reverberation data, and then train RNN-DAE
Speaker recognition
- DNN-ivector framework
- SUSR
- AutoEncoder + metric learning
- binary ivector
language vector
- write a paper--zhiyuan
- hold
- language vector is added to multi hidden layers--zhiyuan
- write code done
- check code
- http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=480
- RNN language vector
- hold
- language vector into multiple layers --Zhiyuan
- a Chinese paper
- speech rate into multiple layers --Zhiyuan
- verify the code for extra input(s) into DNN
multi-GPU
- multi-stream training --Sheng Su
- write a technique report
- kaldi-nnet3 --Xuewei
- 7*2048 8k 1400h tdnn training Xent done
- nnet3 mpe code is under investigation
- http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=472
- AM training on big dataset --mengyuan
- fix decode bug
- nnet3 lstm & blstm training on sinovoice 120h dataset, using KALDI's default config. but result is worse than tdnn
- train mpe --Zhiyong,Xuewei
- train nnet3 mpe using data from Jietong--Xuewei
- modify code to print stats
- modify code to reduce memory
multi-task
- test according to selt-information neural structure learning --mengyuan
- hold
- write code done
- no significant performance improvement observed
- speech rate learning --xiangyu
- hold
- no significant performance improvement observed
- http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=483
- get results with extra input of speech rate info --Zhiyuan
Text Processing
Work
RNN Poem Process
- Combine addition rhyme.
- Investigate new method.
Document Represent
- Code done. Wait some experiments result.
Seq to Seq
- Work on some tasks.
Order representation
- Code some idea.
Balance Representation
- Investigate some papers.
- Current solution : Use knowledge or large corpus's similar pair.
Hold
Neural Based Document Classification
RNN Rank Task
Graph RNN
- Entity path embeded to entity.
- (hold)
RNN Word Segment
- Set bound to word segment.
- (hold)
Recommendation
- Reproduce baseline.
- LDA matrix dissovle.
- LDA (Text classification & Recommendation System) --> AAAI
RNN based QA
- Read Source Code.
- Attention based QA.
- Coding.
Text Group Intern Project
Buddhist Process
- (hold)
RNN Poem Process
- Done by Haichao yu & Chaoyuan zuo Mentor : Tianyi Luo.
RNN Document Vector
- (hold)
Image Baseline
- Demo Release.
- Paper Report.
- Read CNN Paper.
Text Intuitive Idea
Trace Learning
- (Hold)
Match RNN
- (Hold)
financial group
model research
- RNN
- online model, update everyday
- modify cost function and learning method
- add more feature
rule combination
- GA method to optimize the model
basic rule
- classical tenth model
multiple-factor
- add more factor
- use sparse model
display
- bug fixed
- buy rule fixed
data
- data api
- download the future data and factor data
