ANN Tutorial
語音訊號處理之初步實驗
NTU Speech Lab
指導教授: 李琳山
助教: 熊信寬
ㄊ
ㄞ
ㄨ
ㄢ
ㄉ ㄒ
ㄚ ㄩ
ㄝ
Acoustic Model
台
灣
大
Language Model
學
Speech Recognition
Classifier
ㄅㄆㄇㄈ
ㄉㄊㄋㄌ
ㄍㄎㄏ
ㄐㄑㄒ
ㄓㄔㄕㄖ
ㄗㄘㄙ
ㄧㄨㄩ
ㄚㄛㄜㄝ
ㄞㄟㄠㄡ
ㄢㄣㄤㄥ
ㄦ
How do we build
this machine?
Our Task
• “Field of study that gives computers the ability to
learn without being explicitly programmed”
• Instead of writing a program by hand for each specific task, we
collect lots of examples that specify the correct output for a given
input.
• A machine learning algorithm then takes these examples and
produces a program that does the job.
• The program produced by the learning algorithm may look very
different from a typical hand-written program. It may contain
millions of numbers.
• If we do it right, the program works for new cases as well as the
ones we trained it on.
• If the data changes the program can change too by training on the
new data.
• Massive amounts of computation are now cheaper than paying
someone to write a task-specific program.
Machine Learning
Reference: Neural Networks for Machine Learning course by Geoffrey Hinton, Coursera
Features
•Hair Length
•Make-up
.
.
.
Classifier
Classes
Male
Classifier
Female
Others
The Classification Task
Female
Male
Make-Up
Hair Length
Voice pitch
2D Feature Space
• We need some type of non-linear function!
Multi-D Feature Space
• Each neuron receives inputs from other neurons
• The effect of each input line on the neuron is controlled by a synaptic weight
• The weights can be positive or negative.
• The synaptic weights adapt so that the whole network learns to perform
useful computations
• Recognizing objects, understanding language, making plans, controlling
the body.
Neurons
Reference: Neural Networks for Machine Learning course by Geoffrey Hinton, Coursera
Feed Forward Net
activation
function
x1
x2
x3
w1
w2
w3
i th input
bias
y  f (b   xi wi )
output
y
i
index over
input connections
weight on
i th
w4
b
x4
1
• A lot of simple non-linearity  complex non-linearity
Artificial Neural Network
input
• Sigmoid Function A.K.A Logistic Function
Activation Function
• Intuition:
1. Start with random weights
2. Compare the outputs of the net
to the targets
3. Try to adjust the weights to
match the outputs with the targets
E 
E
w
1
2
 (t
joutput
j
yj
tj
Target
0.2
0
0.9
1
1
4
-3
w
 y j )2
How Do Neural Nets Learn?
x1
x2 w1
w2
x3 w3
x4 w4
Error
w
2
w
1
wt 1  wt  
E
w
Updated weights
Learning rate
Gradient Descent
Reference: Machine Learning course by Andrew Ng, Coursera
E dy j E
E

 y j (1  y j )
z j dz j y j
y j
j
zj
i yi
E 
1
2
 (t
joutput
j
 y j )2
yj
¶E
=
¶yi
dz j ¶E
å dy ¶z =
i
j
j
å wij
j
¶z j ¶E
¶E
¶E
=
= yi
¶wij
¶wij ¶z j
¶z j
E
yout
 (t  yout )
Back Propagation
Reference: Neural Networks for Machine Learning course by Geoffrey Hinton, Coursera
¶E
¶z j
output = y
input = x
• Which model do you trust?
• The complicated model fits the data
better.
• But it is not economical.
• A model is convincing when it fits a lot
of data surprisingly well.
• Not just the training data.
Which output value should you
predict for this test input?
Overfitting
Reference: Neural Networks for Machine Learning course by Geoffrey Hinton, Coursera
• Divide dataset into training set and
validation set
• Use training set for training, validation
set as indicator to overfitting
• Use another independent testing test to
performance
Training and Testing
ㄊ
ㄞ
ㄨ
ㄢ
ㄉ ㄒ
ㄚ ㄩ
ㄝ
Acoustic Model
台
灣
大
Language Model
學
Speech Recognition
ㄅㄆㄇㄈ
ㄉㄊㄋㄌ
ㄍㄎㄏ
ㄐㄑㄒ
ㄓㄔㄕㄖ
ㄗㄘㄙ
ㄧㄨㄩ
ㄚㄛㄜㄝ
ㄞㄟㄠㄡ
ㄢㄣㄤㄥ
ㄦ
Our Task
MFCC:13 Dim Vector
We can ‘splice’ the right
and left 4 MFCCs to form
a 13 x 9 = 117 Dim Vector
Example --- Inputs
• Right context dependent initial final phones
• ‘a’, ‘ai’, ‘an’, ‘ang’, ‘au’, ‘b_a’, ‘b_e’, ‘b_ee’, ‘b_i’ …
117
Dim
MFCC
Example --- Output
Target
‘a’
0
‘ai’
1
‘an’
0
• net = newff(Feature,Label,10);
% given Feature and Label, this command generates an NN
with 10 hidden units
• net = newff(Feature,Label,[20 10],{'logsig' 'tansig'});
% this command generates an NN with two layers, one with 20
units and sigmoid as activation function, one with 10
units and tanh as activation function
Matlab NN toolkit---Net
• [net,tr] = train(net,Feature,Label);
% this command will train the network, dividing your
dataset into training, validation, and testing set
Matlab NN Toolkit ---Train
• out = sim(net,testInputs);
• % this command runs the command you have trained on a
test data set to obtain outputs
Matlab NN Toolkit ---Test
• nntool
% this command calls a GUI for NN visualization
Matlab NN Toolkit ---NNTool
• Location: http://140.112.21.15/~poetek/NNkit/
• Copy the folder to your computer, and run the file ‘run.m’
for a quick demo
Demo Program
DEMO
• Example (1): Classify the inputs into 4 classes: {‘a’, ‘e’, ‘i’, ‘u’}
ans =
correct: 1139 out of 1205
accuracy =
0.9452
classification =
214 3 0 3
13 195 9 6
0 5 377 12
1 11 3 353
Demo Program --- Results
•
Example (2): Classify the inputs into 6 classes: {‘a’, ‘e’, ‘i’, ‘u’, ‘b_a’, ‘p_a’, ‘m_a’, ‘f_a’}
correct: 1293 out of 1446
accuracy =
0.8942
classification =
207 7 0
5 204 8
2 9 397
2 10 2
3 1 1
0 0 0
2 5 2
8 5 2
0 0
16 1
8 0
348 0
1 63
2 5
6 4
5 11
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
14
4
0
74
Demo Program --- Results(2)
• Example (3): Classify the inputs into all 147 classes
correct: 92 out of 506
accuracy =
0.1818
Demo Program --- Results(3)
• Please run example 1~3 with the demo program, record
your performance, run time and your observations
• Please play around with different settings to achieve
better accuracy
Some parameters you can change:
• Net structure
• Training algorithm
• Input Features
• net.trainParam
(Please type ‘help newff’ for more info)
Assignments
• Neural Networks for Machine Learning course by
Geoffrey Hinton, Coursera
• Machine Learning course by Andrew Ng, Coursera
• Matlab NN toolkit documentation
References
• Feel free to ask questions!
My Email: b98901024@ntu.edu.tw
Thank You!