2014-10-20

Speech Processing

AM development

Contour

NAN problem

nan recurrence

  ------------------------------------------------------------
   grid/atr.  |   Reproducible  |    add.
  ------------------------------------------------------------
   grid-10    |     yes         |   
  ------------------------------------------------------------
   grid-12    |     no          | "nan" in different position
  ------------------------------------------------------------
   grid-14    |     yes         |  
  ------------------------------------------------------------

Sparse DNN

Performance improvement found when pruned slightly
Experiments show that
Suggest to use TIMIT / AURORA 4 for training

RNN AM

Initial test on WSJ , leads to out-memory.
Using AURORA 4 short-sentence with a smaller number of targets.

Noise training

First draft of the noisy training journal paper
Paper Correction (Yinshi, Liuchao, Lin Yiye), be going.

Drop out & Rectification & convolutive network

Drop out

dataset:wsj, testset:eval92

       std |  dropout0.4 | dropout0.5 | dropout0.7 | dropout0.8
    ------------------------------------------------------------- 
       4.5 |     5.39    |    4.80    |   4.36     |    -

Test on noisy AURORA4 dataset

       std |  dropout0.4 | dropout0.5 | dropout0.7 | dropout0.8
    ------------------------------------------------------------- 
      6.05 |     -       |    -       |   -        |   -

Continue the droptout on normal trained XEnt NNET , eg wsj. (+)
Draft the dropout-DNN weight distribution. (+)

Rectification

Still NAN error, need to debug. (+)

MaxOut (+)

Convolutive network

Test more configurations
Yiye will work on CNN
Reading CNN tutorial

Denoising & Farfield ASR

ICASSP paper submitted.

VAD

Add more silence tag "#" in pure-silence utterance text(train).

xEntropy model be training
need to test baseline.

Sum all sil-pdf as the silence posterior probability.

Program done, to tune the threshold

Speech rate training

Seems ROS model is superior to the normal one with faster speech
Suggest to extract speech data of different ROS, construct a new test set(+)
Suggest to use Tencent training data(+)

low resource language AM training

Use Chinese NN as initial NN, change the last layer

Various the used Chinese trained DNN layer numbers.
- feature_transform = 6000h_transform + 6000_N*hidden-layers

 nnet.init = random (4-N)*hidden-layers + output-layer
 | N / learn_rate | 0.008         | 0.001 | 0.0001 |
 |   baseline     | 17.00(14*2h)  |       |        |
 |       4        | 17.75(9*0.6h) | 18.64 |        |
 |       3        | 16.85         |       |        |
 |       2        | 16.69         |       |        |
 |       1        | 16.87         |       |        |
 |       0        | 16.88         |       |        |

- feature_transform = uyghur_transform + 6000_N*hidden-layers

 nnet.init = random (4-N)*hidden-layers + output-layer
 Note: This is reproduced Yinshi's experiment
 | N / learn_rate | 0.008 | 0.001 | 0.0001 |
 |   baseline     | 17.00 |       |        |
 |       4        | 28.23 | 30.72 | 37.32  |
 |       3        | 22.40 |       |        |
 |       2        | 19.76 |       |        |
 |       1        | 17.41 |       |        |
 |       0        |       |       |        |

- feature_transform = 6000_transform + 6000_N*hidden-layers

 nnet.init = uyghur (4-N)*hidden-layers + output-layer
 | N / learn_rate | 0.008 | 0.001 | 0.0001 |
 |   baseline     | 17.00 |       |        |
 |       4        | 17.80 | 18.55 | 21.06  |
 |       3        | 16.89 | 17.64 |        |
 |       2        |       |       |        |
 |       1        |       |       |        |
 |       0        |       |       |        |

Scoring

global scoring done.
Pitch & rhythm done, need testing
Harmonics program done, experiment to be done.

Confidence

Reproduce the experiments on fisher dataset.
Use the fisher DNN model to decode all-wsj dataset

Speaker ID

Preparing GMM-based server.

Emotion detection

Sinovoice is implementing the server

Text Processing

LM development

Domain specific LM

h2. lm based on baidu_hi and baiduzhidao is done, test on shujutang test set. h2. weibo lm were training with pruning on counts(5,10,10,20,20),because it is too large. the ppl is twice as high than baidu_hi && baidu_zhidao.

h2. NUM tag LM:

use HIT's LTP tool to segment,pos and ner. the program is running(about 3 days) on baiduHi and baiduzhidao(total 365G)
will use the small test set from xiaoxi for address-tag..
now about more 1M address,will prune it using frequency.

Word2Vector

W2V based doc classification

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

SSA-based local linear mapping still on running.
k-means classes change to 2.

Knowledge vector started

format the data
yuanbin will continue this work with help of xingchao.

Character to word conversion

prepare the task: word similarity
prepare the dict.

Google word vector train

some ideal will discuss on weekly report.

RNN LM

rnn

get baseline on nbest rescore of wer.

lstm+rnn

trained the RNN+LSTM lm on wsj_np_data about 200M. the neural net work is 100*100(lstm cell)*10000 with 100 classes. it cost about 200 minutes each epoch.