asrman

Monday, April 29, 2013

NCE 4 Best sentences for reciting

(Apr. 29, 2013 Begin)
Lesson 1: Find fossil man
1) The only way that they can preserve their history is to recount it as sagas--legends handed down from one generation of storytellers to another.
2) But the first people who were like ourselves lived so long ago that even their sagas, if they had any, are forgotten.

Lesson 2:
1) How much of each year do spiders spend killing insects?
2) Why, you may wonder, should spiders be our friends?
3) Insects would make it impossible for us to live in the world; they would devour all our crops and kill our flock and herds, if it were not for the protection we get from insect-eating animals.
4) Moreover, unlike some of the other insect eaters, spiders never do the least harm to us or our belonging.
5) Spiders are not insects, as many people think, nor even nearly related to them.
6) How many spiders are engaged in this work on our behalf?

Lesson 3:
1) Modern alpinists try to climb mountains by a route which will give them good sport, and the more difficult it is, the more highly it is regarded.
2) In the pioneering days, however, this was not the case at all. The early climbers were looking for the easiest way to the top, because the summit was the prize they sought, especially if it had never been attained before. It is true that during their explorations they often faced difficulties and dangers of the most perilous nature, equipped in a manner which would make a modern climber shudder at the thought, but they did not go out of their way to court such excitement. They had a single aim, a solitary goal--the top!
3) Invariably the background is the same: dirt and poverty, and very uncomfortable.

Lesson 4:
1) Several cases have been reported recently in Russia of people who can read and detect colors with their fingers, and even see through solid doors and walls.
2)Once case concerns an eleven-year-old schoolgirl, Vera Petrova, who has normal vision but who can also perceive things with different parts of her skin.
3) It was also found that although she could perceive things through her fingers this ability ceased the moment her hands were wet.

Lesson 5:
1)People are always talking about 'the problem of youth'. If there is one----which I take leave to doubt----then it is older people who create it, not the young themselves. Let us get down to fundamentals and agree that the young are after all human beings----people just like their elders.There is only one difference between an old man and a young one: the young man has a glorious future before him and the old one has a splendid future behind him: and maybe that is where the rub is.

Lesson 6: The sporting spirit
1) Even if one did not know from concrete examples(the 1936 Olympic Games, for instance), one can deduce it from general principles.
2) Nearly all the sports practiced nowadays are competitive. You play to win, and the game has little meaning unless you do your utmost to win.
3) At the international level, sports is frankly mimic warfare. But the significant thing is not the behavior of the players but the attitude of the spectators and, behind the spectators, of the nations who work themselves into furies over these absurd contests, and seriously believe----at any rate for short periods----that running, jumping and kicking a ball are tests of national virtue.

Lesson 7: Bats
1) To get a full appreciation of what this means we must turn first to some recent human inventions. Everyone knows if he shouts in the vicinity of a wall or mountainside, an echo will come back. The further off this solid obstruction, the longer time will elapse for the return of the echo.
2) So it is a comparatively simple step from locating the sea bottom to location a shoal of fish.

Sunday, April 28, 2013

Inaugural Address (March 4, 1905) Theodore Roosevelt

USA president speech collection
http://millercenter.org/president

http://millercenter.org/president/speeches/detail/3564

不论是国家还是个人,公正和宽厚都是强者而不是弱者的表现.
当我们小心避免伤害别人时,我们也需注意自己不受到伤害_罗斯福

But justice and generosity in a nation, as in an individual, count most when shown not by the weak but by the strong. While ever careful to refrain from wrongdoing others, we must be no less insistent that we are not wronged ourselves.

Friday, April 26, 2013

Doddington's Zoo: Sheep, Goat, Lamb, Wolf

Sheep – Sheep comprise our default speaker type. In our model, sheep dominate the population and systems perform nominally well for them.

Goats – Goats, in our model, are those speakers who are particularly difficult to recognize. Goats tend to adversely affect the performance of systems by accounting for a disproportionate share of the missed detections. The goat population can be an especially important problem for entry control systems, where it is important that all users be reliably accepted.

Lambs – Lambs, in our model, are those speakers who are particularly easy to imitate. That is, a randomly chosen speaker is exceptionally likely to be accepted as a lamb. Lambs tend to adversely affect the performance of systems by accounting for a disproportionate share of the false alarms. This represents a

potential system weakness, if lambs can be identified, either through trial and error or through correlation with other directly observable characteristics.

Wolves – Wolves, in our model, are those speakers who are particularly successful at imitating other speakers. That is, their speech is exceptionally likely to be accepted as that of another speaker. Wolves tend to adversely affect the performance of systems by accounting for a disproportionate share of the false alarms. This represents a potential system weakness, if wolves can be identified and recruited to defeat systems.

Ref:

Doddington, George, et al. Sheep, goats, lambs and wolves: A statistical analysis of speaker performance in the NIST 1998 speaker recognition evaluation. NATIONAL INST OF STANDARDS AND TECHNOLOGY GAITHERSBURG MD, 1998.

quick tutorial on matlab parfor

Step 0: Please make sure you

Step 1: prepare the parallel environment(suppose you have 8 cores)

matlabpool(8)

Step 2: Just replace all the parallelable "for" keyword into "parfor"
Make sure the jobs are independent each other, for example:

Ste 3: Run you script

tic

parfor i = 1:8
c(:,i) = eig(rand(1000));
end
toc

tic

for i = 1:8
c(:,i) = eig(rand(1000));
end

toc

%% Results:

Elapsed time is 3.718411 seconds.
Elapsed time is 5.671640 seconds

Conclusion:

The second group scripts are significantly slower than 1st group.

Reference:
http://www.mathworks.com/help/distcomp/getting-started-with-parfor.html#brb2x57

Thursday, April 25, 2013

results for a full DBN experiment

python code/DBN_small.py
Downloading data from http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz
... loading data
... building the model
... getting the pretraining functions
... pre-training the model
Pre-training layer 0, epoch 0, cost -98.605633351
Pre-training layer 0, epoch 1, cost -83.821740962

...
Pre-training layer 0, epoch 98, cost -68.5052478816
Pre-training layer 0, epoch 99, cost -68.519563826
Pre-training layer 1, epoch 0, cost -171.643871631
Pre-training layer 1, epoch 1, cost -149.515493823
Pre-training layer 1, epoch 2, cost -144.451413679
Pre-training layer 1, epoch 3, cost -141.756252063
Pre-training layer 1, epoch 4, cost -139.878107992
Pre-training layer 1, epoch 5, cost -138.535599652
Pre-training layer 1, epoch 6, cost -137.402074458
Pre-training layer 1, epoch 7, cost -136.497999148
Pre-training layer 1, epoch 8, cost -135.716982528
Pre-training layer 1, epoch 9, cost -135.051954135
Pre-training layer 1, epoch 10, cost -134.447771265
Pre-training layer 1, epoch 11, cost -133.973466419
Pre-training layer 1, epoch 12, cost -133.551518055
Pre-training layer 1, epoch 13, cost -133.139880511
Pre-training layer 1, epoch 14, cost -132.788173252
Pre-training layer 1, epoch 15, cost -132.489801008
Pre-training layer 1, epoch 16, cost -132.237055108
Pre-training layer 1, epoch 17, cost -131.975347485
Pre-training layer 1, epoch 18, cost -131.771333127
Pre-training layer 1, epoch 19, cost -131.540889258
Pre-training layer 1, epoch 20, cost -131.384947825
Pre-training layer 1, epoch 21, cost -131.225772659
Pre-training layer 1, epoch 22, cost -131.070327762
Pre-training layer 1, epoch 23, cost -130.941930783
Pre-training layer 1, epoch 24, cost -130.778483759
Pre-training layer 1, epoch 25, cost -130.668305024
Pre-training layer 1, epoch 26, cost -130.555502214
Pre-training layer 1, epoch 27, cost -130.478535277
Pre-training layer 1, epoch 28, cost -130.364395118
Pre-training layer 1, epoch 29, cost -130.287104187
Pre-training layer 1, epoch 30, cost -130.210807909
Pre-training layer 1, epoch 31, cost -130.107654161
Pre-training layer 1, epoch 32, cost -130.028662833
Pre-training layer 1, epoch 33, cost -129.992199401
Pre-training layer 1, epoch 34, cost -129.88685884
Pre-training layer 1, epoch 35, cost -129.847813521
Pre-training layer 1, epoch 36, cost -129.786785169
Pre-training layer 1, epoch 37, cost -129.730273604
Pre-training layer 1, epoch 38, cost -129.697135786
Pre-training layer 1, epoch 39, cost -129.636278175
Pre-training layer 1, epoch 40, cost -129.618305605
Pre-training layer 1, epoch 41, cost -129.551387057
Pre-training layer 1, epoch 42, cost -129.49368135
Pre-training layer 1, epoch 43, cost -129.476903705
Pre-training layer 1, epoch 44, cost -129.425981055
Pre-training layer 1, epoch 45, cost -129.389322047
Pre-training layer 1, epoch 46, cost -129.362279036
Pre-training layer 1, epoch 47, cost -129.337946502
Pre-training layer 1, epoch 48, cost -129.304442912
Pre-training layer 1, epoch 49, cost -129.260127775
Pre-training layer 1, epoch 50, cost -129.221536862
Pre-training layer 1, epoch 51, cost -129.210379139
Pre-training layer 1, epoch 52, cost -129.169092219
Pre-training layer 1, epoch 53, cost -129.131854654
Pre-training layer 1, epoch 54, cost -129.133793916
Pre-training layer 1, epoch 55, cost -129.099146517
Pre-training layer 1, epoch 56, cost -129.087258479
Pre-training layer 1, epoch 57, cost -129.060194224
Pre-training layer 1, epoch 58, cost -129.027875632
Pre-training layer 1, epoch 59, cost -128.995649811
Pre-training layer 1, epoch 60, cost -128.977655279
Pre-training layer 1, epoch 61, cost -128.945129528
Pre-training layer 1, epoch 62, cost -128.940161975
Pre-training layer 1, epoch 63, cost -128.939732915
Pre-training layer 1, epoch 64, cost -128.941077969
Pre-training layer 1, epoch 65, cost -128.902307804
Pre-training layer 1, epoch 66, cost -128.868177422
Pre-training layer 1, epoch 67, cost -128.874653731
Pre-training layer 1, epoch 68, cost -128.867196338
Pre-training layer 1, epoch 69, cost -128.864623846
Pre-training layer 1, epoch 70, cost -128.843517828
Pre-training layer 1, epoch 71, cost -128.827557045
Pre-training layer 1, epoch 72, cost -128.79131154
Pre-training layer 1, epoch 73, cost -128.773199697
Pre-training layer 1, epoch 74, cost -128.769646485
Pre-training layer 1, epoch 75, cost -128.776708309
Pre-training layer 1, epoch 76, cost -128.759387417
Pre-training layer 1, epoch 77, cost -128.714281256
Pre-training layer 1, epoch 78, cost -128.71048483
Pre-training layer 1, epoch 79, cost -128.731262644
Pre-training layer 1, epoch 80, cost -128.701462441
Pre-training layer 1, epoch 81, cost -128.666946237
Pre-training layer 1, epoch 82, cost -128.682094488
Pre-training layer 1, epoch 83, cost -128.677214794
Pre-training layer 1, epoch 84, cost -128.66396384
Pre-training layer 1, epoch 85, cost -128.65356571
Pre-training layer 1, epoch 86, cost -128.6662782
Pre-training layer 1, epoch 87, cost -128.618077204
Pre-training layer 1, epoch 88, cost -128.607518439
Pre-training layer 1, epoch 89, cost -128.640183712
Pre-training layer 1, epoch 90, cost -128.619865401
Pre-training layer 1, epoch 91, cost -128.62696618
Pre-training layer 1, epoch 92, cost -128.599751823
Pre-training layer 1, epoch 93, cost -128.591895957
Pre-training layer 1, epoch 94, cost -128.554581802
Pre-training layer 1, epoch 95, cost -128.555603175
Pre-training layer 1, epoch 96, cost -128.565845135
Pre-training layer 1, epoch 97, cost -128.544153488
Pre-training layer 1, epoch 98, cost -128.548815867
Pre-training layer 1, epoch 99, cost -128.560006264
Pre-training layer 2, epoch 0, cost -70.0880996936
Pre-training layer 2, epoch 1, cost -57.9429660768
Pre-training layer 2, epoch 2, cost -55.4440582901
Pre-training layer 2, epoch 3, cost -54.1089682447
Pre-training layer 2, epoch 4, cost -53.2713100853
Pre-training layer 2, epoch 5, cost -52.6138329286
Pre-training layer 2, epoch 6, cost -52.1256050418
Pre-training layer 2, epoch 7, cost -51.719914925
Pre-training layer 2, epoch 8, cost -51.3744889513
Pre-training layer 2, epoch 9, cost -51.0875345829
Pre-training layer 2, epoch 10, cost -50.7886417596
Pre-training layer 2, epoch 11, cost -50.5449013346
Pre-training layer 2, epoch 12, cost -50.3558212492
Pre-training layer 2, epoch 13, cost -50.1790201092
Pre-training layer 2, epoch 14, cost -49.9945276798
Pre-training layer 2, epoch 15, cost -49.8243693353
Pre-training layer 2, epoch 16, cost -49.6824833823
Pre-training layer 2, epoch 17, cost -49.5411691089
Pre-training layer 2, epoch 18, cost -49.4336682825
Pre-training layer 2, epoch 19, cost -49.3077187712
Pre-training layer 2, epoch 20, cost -49.184447665
Pre-training layer 2, epoch 21, cost -49.1042597712
Pre-training layer 2, epoch 22, cost -48.9922685439
Pre-training layer 2, epoch 23, cost -48.9134351854
Pre-training layer 2, epoch 24, cost -48.8040250662
Pre-training layer 2, epoch 25, cost -48.7458521848
Pre-training layer 2, epoch 26, cost -48.6870817501
Pre-training layer 2, epoch 27, cost -48.5984471639
Pre-training layer 2, epoch 28, cost -48.5314310613
Pre-training layer 2, epoch 29, cost -48.4915239528
Pre-training layer 2, epoch 30, cost -48.4237038956
Pre-training layer 2, epoch 31, cost -48.3788210521
Pre-training layer 2, epoch 32, cost -48.3353179124
Pre-training layer 2, epoch 33, cost -48.2622428232
Pre-training layer 2, epoch 34, cost -48.2177285838
Pre-training layer 2, epoch 35, cost -48.1769980633
Pre-training layer 2, epoch 36, cost -48.1261212064
Pre-training layer 2, epoch 37, cost -48.0982768957
Pre-training layer 2, epoch 38, cost -48.0616504145
Pre-training layer 2, epoch 39, cost -48.0000899992
Pre-training layer 2, epoch 40, cost -47.9540308821
Pre-training layer 2, epoch 41, cost -47.9264332891
Pre-training layer 2, epoch 42, cost -47.896046838
Pre-training layer 2, epoch 43, cost -47.8527590216
Pre-training layer 2, epoch 44, cost -47.8439895524
Pre-training layer 2, epoch 45, cost -47.7944288355
Pre-training layer 2, epoch 46, cost -47.7490626803
Pre-training layer 2, epoch 47, cost -47.7302210646
Pre-training layer 2, epoch 48, cost -47.7173249508
Pre-training layer 2, epoch 49, cost -47.6623472115
Pre-training layer 2, epoch 50, cost -47.6406938763
Pre-training layer 2, epoch 51, cost -47.6004131255
Pre-training layer 2, epoch 52, cost -47.5870140934
Pre-training layer 2, epoch 53, cost -47.5679143658
Pre-training layer 2, epoch 54, cost -47.5403547917
Pre-training layer 2, epoch 55, cost -47.5121944423
Pre-training layer 2, epoch 56, cost -47.5091982126
Pre-training layer 2, epoch 57, cost -47.4731519626
Pre-training layer 2, epoch 58, cost -47.4387040136
Pre-training layer 2, epoch 59, cost -47.4307169026
Pre-training layer 2, epoch 60, cost -47.4255378595
Pre-training layer 2, epoch 61, cost -47.3967116458
Pre-training layer 2, epoch 62, cost -47.3831292468
Pre-training layer 2, epoch 63, cost -47.3586719299
Pre-training layer 2, epoch 64, cost -47.3250683865
Pre-training layer 2, epoch 65, cost -47.3121818155
Pre-training layer 2, epoch 66, cost -47.3253935473
Pre-training layer 2, epoch 67, cost -47.287971487
Pre-training layer 2, epoch 68, cost -47.2598606592
Pre-training layer 2, epoch 69, cost -47.2452731909
Pre-training layer 2, epoch 70, cost -47.2544936942
Pre-training layer 2, epoch 71, cost -47.2097531117
Pre-training layer 2, epoch 72, cost -47.2062705306
Pre-training layer 2, epoch 73, cost -47.2004000095
Pre-training layer 2, epoch 74, cost -47.2022627461
Pre-training layer 2, epoch 75, cost -47.1479103501
Pre-training layer 2, epoch 76, cost -47.1796911954
Pre-training layer 2, epoch 77, cost -47.1508054459
Pre-training layer 2, epoch 78, cost -47.1542654028
Pre-training layer 2, epoch 79, cost -47.1457913565
Pre-training layer 2, epoch 80, cost -47.1093583361
Pre-training layer 2, epoch 81, cost -47.1113689527
Pre-training layer 2, epoch 82, cost -47.0899995027
Pre-training layer 2, epoch 83, cost -47.0954480194
Pre-training layer 2, epoch 84, cost -47.072423908
Pre-training layer 2, epoch 85, cost -47.0470443175
Pre-training layer 2, epoch 86, cost -47.0431776517
Pre-training layer 2, epoch 87, cost -46.9994554695
Pre-training layer 2, epoch 88, cost -47.0142823816
Pre-training layer 2, epoch 89, cost -46.9953037602
Pre-training layer 2, epoch 90, cost -46.9977725268
Pre-training layer 2, epoch 91, cost -47.0004457747
Pre-training layer 2, epoch 92, cost -46.9652256313
Pre-training layer 2, epoch 93, cost -46.9635554107
Pre-training layer 2, epoch 94, cost -46.9435821098
Pre-training layer 2, epoch 95, cost -46.9602020613
Pre-training layer 2, epoch 96, cost -46.9421359975
Pre-training layer 2, epoch 97, cost -46.9499591082
Pre-training layer 2, epoch 98, cost -46.9460360187
Pre-training layer 2, epoch 99, cost -46.9196266617
The pretraining code for file DBN.py ran for 789.28m
... getting the finetuning functions
... finetunning the model
epoch 1, minibatch 5000/5000, validation error 3.090000 %
epoch 1, minibatch 5000/5000, test error of best model 3.450000 %
epoch 2, minibatch 5000/5000, validation error 2.530000 %
epoch 2, minibatch 5000/5000, test error of best model 2.730000 %
epoch 3, minibatch 5000/5000, validation error 2.250000 %
epoch 3, minibatch 5000/5000, test error of best model 2.440000 %
epoch 4, minibatch 5000/5000, validation error 2.050000 %
epoch 4, minibatch 5000/5000, test error of best model 2.210000 %
epoch 5, minibatch 5000/5000, validation error 1.940000 %
epoch 5, minibatch 5000/5000, test error of best model 1.930000 %
epoch 6, minibatch 5000/5000, validation error 1.820000 %
epoch 6, minibatch 5000/5000, test error of best model 1.850000 %
epoch 7, minibatch 5000/5000, validation error 1.690000 %
epoch 7, minibatch 5000/5000, test error of best model 1.860000 %
epoch 8, minibatch 5000/5000, validation error 1.600000 %
epoch 8, minibatch 5000/5000, test error of best model 1.770000 %
epoch 9, minibatch 5000/5000, validation error 1.590000 %
epoch 9, minibatch 5000/5000, test error of best model 1.730000 %
epoch 10, minibatch 5000/5000, validation error 1.550000 %
epoch 10, minibatch 5000/5000, test error of best model 1.670000 %
epoch 11, minibatch 5000/5000, validation error 1.460000 %
epoch 11, minibatch 5000/5000, test error of best model 1.600000 %
epoch 12, minibatch 5000/5000, validation error 1.490000 %
epoch 13, minibatch 5000/5000, validation error 1.490000 %
epoch 14, minibatch 5000/5000, validation error 1.500000 %
epoch 15, minibatch 5000/5000, validation error 1.520000 %
epoch 16, minibatch 5000/5000, validation error 1.500000 %
epoch 17, minibatch 5000/5000, validation error 1.490000 %
epoch 18, minibatch 5000/5000, validation error 1.460000 %
epoch 19, minibatch 5000/5000, validation error 1.440000 %
epoch 19, minibatch 5000/5000, test error of best model 1.520000 %
epoch 20, minibatch 5000/5000, validation error 1.430000 %
epoch 20, minibatch 5000/5000, test error of best model 1.510000 %
epoch 21, minibatch 5000/5000, validation error 1.440000 %
epoch 22, minibatch 5000/5000, validation error 1.440000 %
epoch 23, minibatch 5000/5000, validation error 1.430000 %
epoch 24, minibatch 5000/5000, validation error 1.440000 %
epoch 25, minibatch 5000/5000, validation error 1.440000 %
epoch 26, minibatch 5000/5000, validation error 1.440000 %
epoch 27, minibatch 5000/5000, validation error 1.440000 %
epoch 28, minibatch 5000/5000, validation error 1.450000 %
epoch 29, minibatch 5000/5000, validation error 1.440000 %
epoch 30, minibatch 5000/5000, validation error 1.440000 %
epoch 31, minibatch 5000/5000, validation error 1.410000 %
epoch 31, minibatch 5000/5000, test error of best model 1.410000 %
epoch 32, minibatch 5000/5000, validation error 1.400000 %
epoch 32, minibatch 5000/5000, test error of best model 1.420000 %
epoch 33, minibatch 5000/5000, validation error 1.400000 %
epoch 34, minibatch 5000/5000, validation error 1.400000 %
epoch 35, minibatch 5000/5000, validation error 1.400000 %
epoch 36, minibatch 5000/5000, validation error 1.400000 %
epoch 37, minibatch 5000/5000, validation error 1.390000 %
epoch 37, minibatch 5000/5000, test error of best model 1.390000 %
epoch 38, minibatch 5000/5000, validation error 1.380000 %
epoch 38, minibatch 5000/5000, test error of best model 1.400000 %
epoch 39, minibatch 5000/5000, validation error 1.380000 %
epoch 40, minibatch 5000/5000, validation error 1.380000 %
epoch 41, minibatch 5000/5000, validation error 1.370000 %
epoch 41, minibatch 5000/5000, test error of best model 1.380000 %
epoch 42, minibatch 5000/5000, validation error 1.360000 %
epoch 42, minibatch 5000/5000, test error of best model 1.370000 %
epoch 43, minibatch 5000/5000, validation error 1.360000 %
epoch 44, minibatch 5000/5000, validation error 1.360000 %
epoch 45, minibatch 5000/5000, validation error 1.360000 %
epoch 46, minibatch 5000/5000, validation error 1.360000 %
epoch 47, minibatch 5000/5000, validation error 1.360000 %
epoch 48, minibatch 5000/5000, validation error 1.360000 %
epoch 49, minibatch 5000/5000, validation error 1.360000 %
epoch 50, minibatch 5000/5000, validation error 1.360000 %
epoch 51, minibatch 5000/5000, validation error 1.370000 %
epoch 52, minibatch 5000/5000, validation error 1.370000 %
epoch 53, minibatch 5000/5000, validation error 1.380000 %
epoch 54, minibatch 5000/5000, validation error 1.380000 %
epoch 55, minibatch 5000/5000, validation error 1.370000 %
epoch 56, minibatch 5000/5000, validation error 1.370000 %
epoch 57, minibatch 5000/5000, validation error 1.360000 %
epoch 58, minibatch 5000/5000, validation error 1.360000 %
epoch 59, minibatch 5000/5000, validation error 1.370000 %
epoch 60, minibatch 5000/5000, validation error 1.370000 %
epoch 61, minibatch 5000/5000, validation error 1.360000 %
epoch 62, minibatch 5000/5000, validation error 1.350000 %
epoch 62, minibatch 5000/5000, test error of best model 1.300000 %
epoch 63, minibatch 5000/5000, validation error 1.350000 %
epoch 64, minibatch 5000/5000, validation error 1.360000 %
epoch 65, minibatch 5000/5000, validation error 1.360000 %
epoch 66, minibatch 5000/5000, validation error 1.360000 %
epoch 67, minibatch 5000/5000, validation error 1.350000 %
epoch 68, minibatch 5000/5000, validation error 1.340000 %
epoch 68, minibatch 5000/5000, test error of best model 1.290000 %
epoch 69, minibatch 5000/5000, validation error 1.340000 %
epoch 70, minibatch 5000/5000, validation error 1.340000 %
epoch 71, minibatch 5000/5000, validation error 1.340000 %
epoch 72, minibatch 5000/5000, validation error 1.340000 %
epoch 73, minibatch 5000/5000, validation error 1.340000 %
epoch 74, minibatch 5000/5000, validation error 1.340000 %
epoch 75, minibatch 5000/5000, validation error 1.340000 %
epoch 76, minibatch 5000/5000, validation error 1.330000 %
epoch 76, minibatch 5000/5000, test error of best model 1.300000 %
epoch 77, minibatch 5000/5000, validation error 1.330000 %
epoch 78, minibatch 5000/5000, validation error 1.330000 %
epoch 79, minibatch 5000/5000, validation error 1.330000 %
epoch 80, minibatch 5000/5000, validation error 1.330000 %
epoch 81, minibatch 5000/5000, validation error 1.330000 %
epoch 82, minibatch 5000/5000, validation error 1.320000 %
epoch 82, minibatch 5000/5000, test error of best model 1.310000 %
epoch 83, minibatch 5000/5000, validation error 1.320000 %
epoch 84, minibatch 5000/5000, validation error 1.320000 %
epoch 85, minibatch 5000/5000, validation error 1.310000 %
epoch 85, minibatch 5000/5000, test error of best model 1.300000 %
epoch 86, minibatch 5000/5000, validation error 1.310000 %
epoch 87, minibatch 5000/5000, validation error 1.300000 %
epoch 87, minibatch 5000/5000, test error of best model 1.300000 %
epoch 88, minibatch 5000/5000, validation error 1.300000 %
epoch 89, minibatch 5000/5000, validation error 1.300000 %
epoch 90, minibatch 5000/5000, validation error 1.300000 %
epoch 91, minibatch 5000/5000, validation error 1.300000 %
epoch 92, minibatch 5000/5000, validation error 1.300000 %
epoch 93, minibatch 5000/5000, validation error 1.300000 %
epoch 94, minibatch 5000/5000, validation error 1.300000 %
epoch 95, minibatch 5000/5000, validation error 1.300000 %
epoch 96, minibatch 5000/5000, validation error 1.300000 %
epoch 97, minibatch 5000/5000, validation error 1.300000 %
epoch 98, minibatch 5000/5000, validation error 1.300000 %
epoch 99, minibatch 5000/5000, validation error 1.300000 %
epoch 100, minibatch 5000/5000, validation error 1.300000 %
epoch 101, minibatch 5000/5000, validation error 1.300000 %
epoch 102, minibatch 5000/5000, validation error 1.300000 %
epoch 103, minibatch 5000/5000, validation error 1.300000 %
epoch 104, minibatch 5000/5000, validation error 1.300000 %
epoch 105, minibatch 5000/5000, validation error 1.300000 %
epoch 106, minibatch 5000/5000, validation error 1.290000 %
epoch 106, minibatch 5000/5000, test error of best model 1.290000 %
epoch 107, minibatch 5000/5000, validation error 1.280000 %
epoch 107, minibatch 5000/5000, test error of best model 1.290000 %
epoch 108, minibatch 5000/5000, validation error 1.280000 %
epoch 109, minibatch 5000/5000, validation error 1.290000 %
epoch 110, minibatch 5000/5000, validation error 1.290000 %
epoch 111, minibatch 5000/5000, validation error 1.290000 %
epoch 112, minibatch 5000/5000, validation error 1.290000 %
epoch 113, minibatch 5000/5000, validation error 1.290000 %
epoch 114, minibatch 5000/5000, validation error 1.290000 %
epoch 115, minibatch 5000/5000, validation error 1.290000 %
epoch 116, minibatch 5000/5000, validation error 1.290000 %
epoch 117, minibatch 5000/5000, validation error 1.290000 %
epoch 118, minibatch 5000/5000, validation error 1.290000 %
epoch 119, minibatch 5000/5000, validation error 1.290000 %
epoch 120, minibatch 5000/5000, validation error 1.290000 %
epoch 121, minibatch 5000/5000, validation error 1.290000 %
epoch 122, minibatch 5000/5000, validation error 1.290000 %
epoch 123, minibatch 5000/5000, validation error 1.290000 %
epoch 124, minibatch 5000/5000, validation error 1.290000 %
epoch 125, minibatch 5000/5000, validation error 1.290000 %
epoch 126, minibatch 5000/5000, validation error 1.290000 %
epoch 127, minibatch 5000/5000, validation error 1.290000 %
epoch 128, minibatch 5000/5000, validation error 1.300000 %
epoch 129, minibatch 5000/5000, validation error 1.300000 %
epoch 130, minibatch 5000/5000, validation error 1.300000 %
epoch 131, minibatch 5000/5000, validation error 1.300000 %
epoch 132, minibatch 5000/5000, validation error 1.300000 %
epoch 133, minibatch 5000/5000, validation error 1.300000 %
epoch 134, minibatch 5000/5000, validation error 1.300000 %
epoch 135, minibatch 5000/5000, validation error 1.300000 %
epoch 136, minibatch 5000/5000, validation error 1.300000 %
epoch 137, minibatch 5000/5000, validation error 1.300000 %
epoch 138, minibatch 5000/5000, validation error 1.300000 %
epoch 139, minibatch 5000/5000, validation error 1.300000 %
epoch 140, minibatch 5000/5000, validation error 1.300000 %
epoch 141, minibatch 5000/5000, validation error 1.300000 %
epoch 142, minibatch 5000/5000, validation error 1.300000 %
epoch 143, minibatch 5000/5000, validation error 1.300000 %
epoch 144, minibatch 5000/5000, validation error 1.300000 %
epoch 145, minibatch 5000/5000, validation error 1.300000 %
epoch 146, minibatch 5000/5000, validation error 1.300000 %
epoch 147, minibatch 5000/5000, validation error 1.300000 %
epoch 148, minibatch 5000/5000, validation error 1.300000 %
epoch 149, minibatch 5000/5000, validation error 1.300000 %
epoch 150, minibatch 5000/5000, validation error 1.300000 %
epoch 151, minibatch 5000/5000, validation error 1.300000 %
epoch 152, minibatch 5000/5000, validation error 1.300000 %
epoch 153, minibatch 5000/5000, validation error 1.300000 %
epoch 154, minibatch 5000/5000, validation error 1.300000 %
epoch 155, minibatch 5000/5000, validation error 1.300000 %
epoch 156, minibatch 5000/5000, validation error 1.300000 %
epoch 157, minibatch 5000/5000, validation error 1.300000 %
epoch 158, minibatch 5000/5000, validation error 1.300000 %
epoch 159, minibatch 5000/5000, validation error 1.300000 %
epoch 160, minibatch 5000/5000, validation error 1.300000 %
epoch 161, minibatch 5000/5000, validation error 1.300000 %
epoch 162, minibatch 5000/5000, validation error 1.300000 %
epoch 163, minibatch 5000/5000, validation error 1.300000 %
epoch 164, minibatch 5000/5000, validation error 1.300000 %
epoch 165, minibatch 5000/5000, validation error 1.300000 %
epoch 166, minibatch 5000/5000, validation error 1.300000 %
epoch 167, minibatch 5000/5000, validation error 1.300000 %
epoch 168, minibatch 5000/5000, validation error 1.300000 %
epoch 169, minibatch 5000/5000, validation error 1.300000 %
epoch 170, minibatch 5000/5000, validation error 1.300000 %
epoch 171, minibatch 5000/5000, validation error 1.300000 %
epoch 172, minibatch 5000/5000, validation error 1.300000 %
epoch 173, minibatch 5000/5000, validation error 1.300000 %
epoch 174, minibatch 5000/5000, validation error 1.300000 %
epoch 175, minibatch 5000/5000, validation error 1.300000 %
epoch 176, minibatch 5000/5000, validation error 1.300000 %
epoch 177, minibatch 5000/5000, validation error 1.300000 %
epoch 178, minibatch 5000/5000, validation error 1.300000 %
epoch 179, minibatch 5000/5000, validation error 1.300000 %
epoch 180, minibatch 5000/5000, validation error 1.300000 %
epoch 181, minibatch 5000/5000, validation error 1.300000 %
epoch 182, minibatch 5000/5000, validation error 1.300000 %
epoch 183, minibatch 5000/5000, validation error 1.290000 %
epoch 184, minibatch 5000/5000, validation error 1.280000 %
epoch 185, minibatch 5000/5000, validation error 1.280000 %
epoch 186, minibatch 5000/5000, validation error 1.280000 %
epoch 187, minibatch 5000/5000, validation error 1.280000 %
epoch 188, minibatch 5000/5000, validation error 1.280000 %
epoch 189, minibatch 5000/5000, validation error 1.280000 %
epoch 190, minibatch 5000/5000, validation error 1.280000 %
epoch 191, minibatch 5000/5000, validation error 1.280000 %
epoch 192, minibatch 5000/5000, validation error 1.280000 %
epoch 193, minibatch 5000/5000, validation error 1.280000 %
epoch 194, minibatch 5000/5000, validation error 1.280000 %
epoch 195, minibatch 5000/5000, validation error 1.280000 %
epoch 196, minibatch 5000/5000, validation error 1.280000 %
epoch 197, minibatch 5000/5000, validation error 1.280000 %
epoch 198, minibatch 5000/5000, validation error 1.280000 %
epoch 199, minibatch 5000/5000, validation error 1.280000 %
epoch 200, minibatch 5000/5000, validation error 1.280000 %
epoch 201, minibatch 5000/5000, validation error 1.280000 %
epoch 202, minibatch 5000/5000, validation error 1.280000 %
epoch 203, minibatch 5000/5000, validation error 1.280000 %
epoch 204, minibatch 5000/5000, validation error 1.280000 %
epoch 205, minibatch 5000/5000, validation error 1.280000 %
epoch 206, minibatch 5000/5000, validation error 1.280000 %
epoch 207, minibatch 5000/5000, validation error 1.280000 %
epoch 208, minibatch 5000/5000, validation error 1.280000 %
epoch 209, minibatch 5000/5000, validation error 1.280000 %
epoch 210, minibatch 5000/5000, validation error 1.280000 %
epoch 211, minibatch 5000/5000, validation error 1.280000 %
epoch 212, minibatch 5000/5000, validation error 1.280000 %
epoch 213, minibatch 5000/5000, validation error 1.280000 %
Optimization complete with best validation score of 1.280000 %,with test performance 1.290000 %
The fine tuning code for file DBN.py ran for 648.83m
Thu Apr 25 06:23:01 CDT 2013

recipe for NIST SRE 2014

Step 0: Double check you have a better speech audio quality, if you can:
Instead of using the lossy (μlaw) coding, use full-bandwidth version of microphone data (if available) [1]

Step 1: Feature Extraction: Using i-Vector system

Step 2: Back-end Classification: Multi-session back-end [2]

Reference:

[1] Stolcke, Andreas, and Martin Graciarena Luciana Ferrer. "Effects of audio and ASR quality on cepstral and high-level speaker verification systems." Odyssey 2012-The Speaker and Language Recognition Workshop. 2012.[PDF]

[2] G Liu, T Hasan, H Boril, JHL Hansen, "An investigation on back-end for speaker recognition in multi-session enrollment",Proc. IEEE ICASSP2013, Vancouver, Canada, [PDF]

Wednesday, April 24, 2013

results for a small DBN experiment

Note: A full DBN experiment will give result like:

Optimization complete with best validation score of 1.280000 %,with test performance 1.290000 %
The fine tuning code for file DBN.py ran for 648.83m