“Sinovoice-2014-02-25”版本间的差异

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Training Analysis=
第82行: 第82行:
 
* Shared tree GMM model training completed, WER% is similar to non-shared model .
 
* Shared tree GMM model training completed, WER% is similar to non-shared model .
 
* Selected 100h online data, trained two systems: (1) di-syllable system (as the one used in the current training) (2) jt-phone system
 
* Selected 100h online data, trained two systems: (1) di-syllable system (as the one used in the current training) (2) jt-phone system
 +
<pre>
 
         di-syl      jt-ph
 
         di-syl      jt-ph
 
Xent    15.42%      14.78%       
 
Xent    15.42%      14.78%       
第87行: 第88行:
 
MPE2    14.22%
 
MPE2    14.22%
 
MPE3    14.26%
 
MPE3    14.26%
 
+
</pre>
  
 
==Auto Transcription==
 
==Auto Transcription==

2014年2月25日 (二) 07:50的版本

DNN training

Environment setting

  • Two queues: 100.q dedicated to decoding, all.q dedicated to GMM training/MPE lattice generation
  • disk203-disk210: distributed disks, for parallel jobs
  • /nfs/disk1: train212 GPU task; /nfs/disk2: train215 GPU task

Corpora

  • PICC data are under labeling (200h), ready in two weeks.
  • 105h data from BJ mobile
  • Now totally 1121h (470 + 346 + 105 + 200) telephone speech will be ready soon.
  • 16k 6000h data: 978h online data from DataTang + 656h online mobile data + 4300h recording data


470 hour 8k training

470 + 300h + BJ mobile 105h training

(1) 105 BJ mobile re-training without NOISE: 33.97% WER (2) 105 BJ mobile re-training with NOISE phone in training, but decoding without NOISE: 34.27% (3) (2) + noise-decoding (with noise phone in lexicon/LM), still under investigation

BJ mobile incremental training

(1) Original 470 + 300 model: 30.24% WER (2) Incremental DT training with 105h BJ data, 27.01% WER


6000 hour 16k training

Training progress

  • Ran CE DNN to iteration 11 (8400 states, 80000 pdf)
  • Testing results go down to 12.46% WER (Sinovoice results: 10.46).
Model WER RT
small LM, it 4, -5/-9 15.80 1.18
large LM, it 4, -5/-9 15.30 1.50
large LM, it 4, -6/-9 15.36 1.30
large LM, it 4, -7/-9 15.25 1.30
large LM, it 5, -5/-9 14.17 1.10
large LM, it 5, -5/-10 13.77 1.29
large LM, it 6, -5/-9 13.64 -
large LM, it 6, -5/-10 13.25 -
large LM, it 7, -5/-9 13.29 -
large LM, it 7, -5/-10 12.87 -
large LM, it 8, -5/-9 13.09 -
large LM, it 8, -5/-10 12.69 -
large LM, it 9, -5/-9 12.87 -
large LM, it 9, -5/-10 12.55 -
large LM, it 10, -5/-9 12.83 -
large LM, it 10, -5/-10 12.48 -
large LM, it 11, -5/-9 12.87 -
large LM, it 11, -5/-10 12.46 -
  • Additional xEnt training with DNN alignment, should be completed in 2 days
  • DT training is still on the queue, waiting for lattice generation
  • First version of DT model would be trained with online data (1700h)

Training Analysis=

  • Shared tree GMM model training completed, WER% is similar to non-shared model .
  • Selected 100h online data, trained two systems: (1) di-syllable system (as the one used in the current training) (2) jt-phone system
        di-syl      jt-ph
Xent    15.42%      14.78%       
MPE1    14.46%      14.23%
MPE2    14.22%
MPE3    14.26%

Auto Transcription

  • PICC development set decoding obtained 45% WER.
  • PICC auto-trans incremental DT training completed

Threshold WER

org: 45.03% 0.9: 41.89% 0.8: 41.64%

  • Current training data with 0.8 involve 80k sentences, amounting to about 60h data.
  • Sampling 60h labelled data to enrich the training


DNN Decoder

  • Online decoder
  • Integration almost completed
  • Initial CMN implementation finished
  • The first step is to tune the prior prob of the global CMN, and then consider re-training with DT.