“ASR:2014-12-22”版本间的差异

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

details at http://liuc.cslt.org/pages/sparse.html
To conduct MPE-training

RNN AM

Adjusting the learning rate.(+)
Trying toolkit of Microsoft.(+)
details at http://liuc.cslt.org/pages/rnnam.html

A new nnet training scheduler

details at http://liuc.cslt.org/pages/nnet-sched.html
Test 500h dataset, 36-epchs/8-batches --Similar performance observed compared with std recipe
Test on 4000h dataset.

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)

test on XinWenLianBo music. results on
- http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhaomy&step=view_request&cvssid=318

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

@@ 第4行： / 第4行： @@
 ==== Environment ====
 * Already buy 3 760GPU
-* grid-9/12 760GPU crashed again; grid-11 shutdown automatically.
-* Change 760gpu card of grid-12 and grid-14(+).
 * First down-frequency of gpu760.
+* grid-11/12 shut-down automatically
+* Re-exchange GPU760 of grid-12 and grid-14
 ==== Sparse DNN ====
-* Performance improvement found when pruned slightly
-* need retraining for unpruned one; training loss
 * details at http://liuc.cslt.org/pages/sparse.html
+* To conduct MPE-training
 ==== RNN AM====
-* Initial nnet seems not very well, need to be pre-trained or test lower learn-rate.
-* For AURORA 4 1h/epoch, model train done.
-* Using AURORA 4 short-sentence with a smaller number of targets.(+)
 * Adjusting the learning rate.(+)
 * Trying toolkit of Microsoft.(+)
 * details at http://liuc.cslt.org/pages/rnnam.html
-* Reading papers
 ==== A new nnet training scheduler ====
-* Initial code done. No better than original one considering of taking much more iterations.
 * details at http://liuc.cslt.org/pages/nnet-sched.html
+* Test 500h dataset, 36-epchs/8-batches --Similar performance observed compared with std recipe
 * Test on 4000h dataset.
@@ 第30行： / 第25行： @@
 * Drop out(+)
-:* Use different proportion of noise data to investigate the effect of xEnt and mpe and dropout
-:** Problem 1) The effect of dropout in different noise proportion;
-) The effect of MPE in different noise proportion;
-) The effect of MPE+dropout in different noise proportion.
 :**http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?step=view_request&cvssid=261
 :* Conclusion
@@ 第42行： / 第31行： @@
 :** Find and test unknown noise test-data.(++)
-* MaxOut
+* MaxOut && P-norm
-:* Pretraining based maxout, can't use large learning-rate.
-* 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 (+)
+* 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)====
-  (1) train_clean
-    drop-retention/testcase(WER)| test_clean_wv1  | test_airport_wv1 | test_babble_wv1 | test_car_wv1
-   ---------------------------------------------------------------------------------------------------------
-       std-xEnt-sigmoid-baseline| 6.04            |    29.91         |   27.76         | 16.37
-   ---------------------------------------------------------------------------------------------------------
-       std+dae_cmvn_noFT_2-1200 | 7.10            |    15.33         |   16.58         | 9.23
-   ---------------------------------------------------------------------------------------------------------
-    std+dae_cmvn_splice5_2-100  | 8.19            |    15.21         |   15.25         | 9.31
-   ---------------------------------------------------------------------------------------------------------
 :* test on XinWenLianBo music. results on
 :** http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhaomy&step=view_request&cvssid=318
@@ 第75行： / 第69行： @@
 ====Speech rate training====
-* Data ready on tencent set; some errors on speech rate dependent model. error fixed.
+* 64.41->34.4
-* Retrain new model(+)
-====Scoring====
-* Timber Comparison done.
-* harmonics based timber comparison: frequency based feature is better. done
-* GMM based timber comparison is done. Similar to speaker recognition. done
-* TODO: Code checkin and '''technique report'''. done
 ====Confidence====
@@ 第91行： / 第78行： @@
 ===Speaker ID===
-* Preparing GMM-based server.
+:* Non-stream GMM:wer-2.28%
-* EER ~ 4% (GMM-based system)--Text independent
+   seperate3-ivector:wer-3.54 single-ivector:wer-1.57
-* EER ~ 6%(1s) / 0.5%(5s) (GMM-based system)--Text dependent
+   seperate-PLDA:wer-0.87 single-PLDA:wer-1.00
-* test different number of components; fast i-vector computing
+:* Code ready
-:* Test with number recordings, The 256 number component is the best.
-:* Test with text-dependent recordings, The 1024 number component is the best.
-:* Interpolation alpha is not sensitive.
 ===Language ID===