“ASR:2014-12-22”版本间的差异

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Speaker ID
 
第80行: 第80行:
 
:* Non-stream GMM:wer-2.28%  
 
:* Non-stream GMM:wer-2.28%  
 
   seperate3-ivector:wer-3.54 single-ivector:wer-1.57   
 
   seperate3-ivector:wer-3.54 single-ivector:wer-1.57   
   seperate-PLDA:wer-0.87 single-PLDA:wer-1.04    
+
   seperate-PLDA:wer-0.87 single-PLDA:wer-1.00    
:* Code ready                  
+
:* Code ready
  
 
===Language ID===
 
===Language ID===

2014年12月22日 (一) 08:18的最后版本

Speech Processing

AM development

Environment

  • Already buy 3 760GPU
  • First down-frequency of gpu760.
  • grid-11/12 shut-down automatically
  • Re-exchange GPU760 of grid-12 and grid-14

Sparse DNN

RNN AM

A new nnet training scheduler

Dropout & Maxout & Convolutive network

  • Drop out(+)
 Dropout is effective for minority.
    • Find and test unknown noise test-data.(++)
  • MaxOut && P-norm
  • Need to solve the too small learning-rate problem
    • Add one normalization layer after the pnorm-layer
    • Add L2-norm upper bound
  • Convolutive network
  • DAE test: to test various noises(car/echo/airport....)

              | group-size | cnn-output| test_clean_wv1 | test_car_wv1 |test_babble_wv1 | test_airport_wv1

max_out_32 | 64 | 32 | 6.82 | 17.75 |36.77 | 35.61


max_out_128 | 16 | 128 | 6.09 | 15.92 |31.74 | 30.85


max_out_256 | 8 | 256 | 6.38 | 16.47 |31.32 | 31.93


max_out_32_MPE | 64 | 32 | 6.25 | 18.62 |49.07 | 46.25


cnn_layer_3_3                          | 5.73           | 18.09        |30.92           | 30.81

 cnn_std                               | 5.73           | 17.25        |27.59           | 29.07

 dnn_std                               | 6.04           | 16.37        |27.76           | 29.91


DAE(Deep Atuo-Encode)

Denoising & Farfield ASR

  • ICASSP paper submitted.
  • HOLD

VAD

  • Harmonics and Teager energy features being investigation (++)

Speech rate training

  • 64.41->34.4

Confidence

  • Reproduce the experiments on fisher dataset.
  • Use the fisher DNN model to decode all-wsj dataset
  • preparing scoring for puqiang data
  • HOLD

Speaker ID

  • Non-stream GMM:wer-2.28%
  seperate3-ivector:wer-3.54 single-ivector:wer-1.57  
  seperate-PLDA:wer-0.87 single-PLDA:wer-1.00   
  • Code ready

Language ID

  • GMM-based language is ready.
  • Delivered to Jietong
  • Prepare the test-case

Voice Conversion

  • Yiye is reading materials(+)


Text Processing

LM development

Domain specific LM

  • domain lm
  • Sougou2T : kn-count continue .
  • lm v2.0 done,just to test the wer.
  • new dict.

tag LM

  • summary done
  • need to do
  • tag Probability should test add the weight(hanzhenglong) and handover to hanzhenglong (hold)
  • paper done,begin to modify .

RNN LM

  • rnn
  • test wer RNNLM on Chinese data from jietong-data(this week)
  • generate the ngram model from rnnlm and test the ppl with different size txt.[1]
  • lstm+rnn
  • check the lstm-rnnlm code about how to Initialize and update learning rate.(hold)

Word2Vector

W2V based doc classification

  • Initial results variable Bayesian GMM obtained. Performance is not as good as the conventional GMM.(hold)
  • Non-linear inter-language transform: English-Spanish-Czch: wv model training done, transform model on investigation

Knowledge vector

  • Knowledge vector started
  • code done,to test the baseline with a task.
  • problem with weight.

relation

  • Accomplish transE with almost the same performance as the paper did(even better)[2]

Character to word

  • Character to word conversion(hold)
  • prepare the task: word similarity
  • prepare the dict.

Translation

  • v5.0 demo released
  • cut the dict and use new segment-tool

QA

improve fuzzy match

  • add Synonyms similarity using MERT-4 method(hold)

improve lucene search

  • mutli query's performance improve from 66.228 to 68.672. detail:[3]
  • check the MERT problem that doesn't mach the qa

XiaoI framework

  • ner from xiaoI done

query normalization

  • using NER to normalize the word
  • new inter will install SEMPRE