“ASR:2015-02-02”版本间的差异
来自cslt Wiki
（→tag LM） |
（→Text Processing） |
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| 第77行： | 第77行： | ||
* similar word extension in FST | * similar word extension in FST | ||
:* write a draft of a paper | :* write a draft of a paper | ||
| − | :* result | + | :* result [http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=mx&step=view_request&cvssid=332] |
====RNN LM==== | ====RNN LM==== | ||
2015年2月3日 (二) 01:06的版本
目录
Speech Processing
AM development
Environment
- May gpu760 of grid-14 has been repairing.
- grid-11 often shutdown automatically, too slow computation speed.
RNN AM
- details at http://liuc.cslt.org/pages/rnnam.html
Dropout & Maxout & rectifier
- Need to solve the too small learning-rate problem
- 20h small scale sparse dnn with rectifier. --Chao liu
- 20h small scale sparse dnn with Maxout/rectifier based on weight-magnitude-pruning. --Mengyuan Zhao
Convolutive network
- Convolutive network(DAE)
- http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=311
- Technical report to draft, Mian Wang, Yiye Lin, Shi Yin, Mengyuan Zhao
DNN-DAE(Deep Auto-Encode-DNN)
- Technical report to draft, Xiangyu Zeng, Shi Yin, Mengyuan Zhao and Zhiyong Zhang,
- http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=318
RNN-DAE(Deep based Auto-Encode-RNN)
VAD
- DAE
- Technical report --Shi Yin
Speech rate training
- http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=268
- Technical report to draft. Shi Yin
- Prepare for ChinaSIP
Confidence
- Reproduce the experiments on fisher dataset.
- Use the fisher DNN model to decode all-wsj dataset
- preparing scoring for puqiang data
- HOLD
Neural network visulization
Speaker ID
Language ID
- GMM-based language is ready.
- Delivered to Jietong
- Prepare the test-case
- http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=328
Voice Conversion
- Yiye is reading materials
- HOLD
Text Processing
LM development
Domain specific LM
- LM2.X
- mix the sougou2T-lm,kn-discount continue
- train a large lm using 25w-dict.(hanzhenglong/wxx)
- v2.0a adjust the weight and smaller weight of transportation is better.(done)
- v2.0b add the v1.0 vocab(this week)
- v2.0c filter the useless word.(next week)
- set the test set for new word (hold)
tag LM
- Tag Lm
- code is given to jietong .
- similar word extension in FST
- write a draft of a paper
- result [1]
RNN LM
- rnn
- test wer RNNLM on Chinese data from jietong-data
- generate the ngram model from rnnlm and test the ppl with different size txt.
- lstm+rnn
- check the lstm-rnnlm code about how to Initialize and update learning rate.(hold)
Word2Vector
W2V based doc classification
- data prepare.
Knowledge vector
- run the big data
- continue to test, including paragraph vector and relation.
- result: http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=lr&step=view_request&cvssid=326
- prepare the paper.
Character to word
- Character to word conversion(hold)
Translation
- v5.0 demo released
- cut the dict and use new segment-tool
Sparse NN in NLP
- write a technical report(Wednesday) and make a repoert.
QA
improve fuzzy match
- add Synonyms similarity using MERT-4 method(hold)
improve lucene search
- add more feature to improve search.
- POS, NER ,tf ,idf
- result:P@1: 0.68734335-->0.7763158 1097-->1239 (The number of queries is 1596.)P@5: 0.80325814-->0.8383459 1282-->1338 (The number of queries is 1596.)[2]
- extract more features about lexical, syntactic and semantic to improve re-ranking performance.
- using sentence vector, it doesn't work.
context framework
- code for organization
- change to knowledge graph,and learn the D2R tool and JENA
query normalization
- using NER to normalize the word
- new inter will install SEMPRE
