“ASR:2015-12-1”版本间的差异
来自cslt Wiki
（→End to End） |
|||
| 第9行： | 第9行： | ||
:* MPE | :* MPE | ||
:* CTC/nnet3/Kaldi | :* 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==== | ====Adapative learning rate method==== | ||
| 第34行： | 第40行： | ||
* AutoEncoder + metric learning | * AutoEncoder + metric learning | ||
* binary ivector | * binary ivector | ||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
===multi-GPU=== | ===multi-GPU=== | ||
2015年12月7日 (一) 06:21的版本
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
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
- train 7*2048 tdnn using 4000h data --Mengyuan
- train mpe using wsj and aurara4 --Zhiyong,Xuewei
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
- test using extreme data
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
