“ASR:2015-02-02”版本间的差异

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Text Processing
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tag LM
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* similar word extension in FST
 
* similar word extension in FST
 
:* write a draft of a paper  
 
:* write a draft of a paper  
:* result :16.32->10.23
+
:* result (need result)
 +
 
 
====RNN LM====
 
====RNN LM====
 
*rnn
 
*rnn

2015年2月2日 (一) 11:48的版本

Speech Processing

AM development

Environment

  • May gpu760 of grid-14 has been repairing.
  • grid-11 often shutdown automatically, too slow computation speed.

RNN AM

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)

DNN-DAE(Deep Auto-Encode-DNN)

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

VAD

  • DAE
  • Technical report --Shi Yin

Speech rate training

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

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 (need result)

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
  • 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.)[1]
  • 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