ASR:2014-12-22

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