“ASR:2014-12-22”版本间的差异

2014年12月22日 (一) 08:05的版本 （查看源代码） Zhangzy（讨论 | 贡献） ←上一编辑		2014年12月22日 (一) 08:18的最后版本 （查看源代码） Zhangzy（讨论 | 贡献） （→‎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===

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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(+)

• • http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?step=view_request&cvssid=261
• Conclusion

 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....)

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

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

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

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

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

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cnn_layer_3_3                          | 5.73           | 18.09        |30.92           | 30.81

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 cnn_std                               | 5.73           | 17.25        |27.59           | 29.07

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 dnn_std                               | 6.04           | 16.37        |27.76           | 29.91

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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