[PPT] - HMMs for Speech 1 Transitions with Bigrams 2 Decoding Finding PowerPoint Presentation

SLIDE 1

HMMs for Speech

1

SLIDE 2

Transitions with Bigrams

2

SLIDE 3

Decoding

3

Finding the words given the acoustics is an HMM

inference problem

We want to know which state sequence x1:T is most

likely given the evidence e1:T:

From the sequence x, we can simply read off the

words

( ) ( )

1: 1:

* 1: 1: 1: 1: 1:

argmax | argmax ,

T T

T T T x T T x

x p x e p x e = =

SLIDE 4

Parameter Estimation

4

Estimating the distribution of a random variable
Elicitation: ask a human (why is this hard?)
Empirically: use training data (learning!)
E.g.: for each outcome x, look at the empirical rate of

that value:

This is the estimate that maximizes the likelihood of the

data

( ) ( )

count total samples

ML

x p x =

( ) ( )

,

i i

L x p x

θ

θ =∏

( )

1 3

ML

p r =

SLIDE 5

Example: Spam Filter

5

Input: email
Output: spam/ham
Setup:
Get a large collection of

example emails, each labeled “spam” or “ham”

Note: someone has to

hand label all this data!

Want to learn to predict

labels of new, future emails

Features: the attributes

used to make the ham / spam decision

Words: FREE!
Text patterns: $dd, CAPS
Non-text: senderInContacts
……

SLIDE 6

Example: Digit Recognition

6

Input: images / pixel grids
Output: a digit 0-9
Setup:
Get a large collection of example

images, each labeled with a digit

Note: someone has to hand label

all this data!

Want to learn to predict labels of

new, future digit images

Features: the attributes used to

make the digit decision

Pixels: (6,8) = ON
Shape patterns: NumComponents,

AspectRation, NumLoops

……

SLIDE 7

A Digit Recognizer

7

Input: pixel grids
Output: a digit 0-9

SLIDE 8

Classification
Given inputs x, predict labels (classes) y
Examples
Spam detection. input: documents; classes:

spam/ham

OCR. input: images; classes: characters
Medical diagnosis. input: symptoms; classes:

diseases

Autograder. input: codes; output: grades

8

Classification

SLIDE 9

Important Concepts

9

Data: labeled instances, e.g. emails marked spam/ham
Training set
Held out set (we will give examples today)
Test set
Features: attribute-value pairs that characterize each x
Experimentation cycle
Learn parameters (e.g. model probabilities) on training set
(Tune hyperparameters on held-out set)
Compute accuracy of test set
Evaluation
Accuracy: fraction of instances predicted correctly
Overfitting and generalization
Want a classifier which does well on test data
Overfitting: fitting the training data very closely, but not

generalizing well

SLIDE 10

General Naive Bayes

10

A general naive Bayes model:
We only specify how each feature depends on

the class

Total number of parameters is linear in n

Y × F

n

parameters

p Y,F

1Fn

( ) =

p Y

( )

p F

i |Y

( )

i

∏

Y parameters n × Y × F parameters

SLIDE 11

General Naive Bayes

11

What do we need in order to use naive Bayes?
Inference (you know this part)
Start with a bunch of conditionals, p(Y) and the p(Fi|Y) tables
Use standard inference to compute p(Y|F1…Fn)
Nothing new here
Learning: Estimates of local conditional probability

tables

p(Y), the prior over labels
p(Fi|Y) for each feature (evidence variable)
These probabilities are collectively called the parameters of

the model and denoted by θ

SLIDE 12

Inference for Naive Bayes

12

Goal: compute posterior over causes
Step 1: get joint probability of causes and evidence
Step 2: get probability of evidence
Step 3: renormalize

p Y, f1 fn

( ) =

p y1, f1 fn

( )

p y2, f1 fn

( )

 p yk, f1 fn

( )

! " # # # # # # $ % & & & & & &

p y1

( )

p fi | c1

( )

i

∏

p y2

( )

p fi | c2

( )

i

∏

 p yk

( )

p fi | ck

( )

i

∏

" # $ $ $ $ $ $ $ $ % & ' ' ' ' ' ' ' '

p f

1 f n

( )

p Y | f1 fn

( )

divide

SLIDE 13

Naive Bayes for Digits

13

Simple version:
One feature Fij for each grid position <i,j>
Possible feature values are on / off, based on whether

intensity is more or less than 0.5 in underlying image

Each input maps to a feature vector, e.g.
Here: lots of features, each is binary valued
Naive Bayes model:

→ F

0,0 = 0 F 0,1 = 0 F 0,2 =1 F 0,3 =1 F 0,4 = 0 F 15,15 = 0

p Y | F

0,0F 15,15

( )∝ p Y ( )

p F

i, j |Y

( )

i, j

∏

SLIDE 14

p(Y=y)
approximated by the frequency of each Y in

training data

p(F|Y=y)
approximated by the frequency of (y,F)

14

Learning in NB (Without smoothing)

SLIDE 15

Examples: CPTs

15

1 0.1 2 0.1 3 0.1 4 0.1 5 0.1 6 0.1 7 0.1 8 0.1 9 0.1 0.1

( )

p Y

( )

3,1

| p F

n Y

=

1 0.05 2 0.01 3 0.90 4 0.80 5 0.90 6 0.90 7 0.25 8 0.85 9 0.60 0.80

( )

5,5

| p F

n Y

=

1 0.01 2 0.05 3 0.05 4 0.30 5 0.80 6 0.90 7 0.05 8 0.60 9 0.50 0.80

SLIDE 16

Example: Spam Filter

16

Naive Bayes spam

filter

Data:
Collection of emails labeled

spam or ham

Note: some one has to hand

label all this data!

Split into training, held-out,

test sets

Classifiers
Learn on the training set
(Tune it on a held-out set)
Test it on new emails

SLIDE 17

Naive Bayes for Text

17

Bag-of-Words Naive Bayes:
Features: Wi is the word at position i
Predict unknown class label (spam vs. ham)
Each Wi is identically distributed
Generative model:
Tied distributions and bag-of-words
Usually, each variable gets its own conditional

probability distribution p(F|Y)

In a bag-of-words model
Each position is identically distributed
All positions share the same conditional probs p(W|C)

p C,W

1Wn

( ) = p C ( )

p Wi |C

( )

i

∏

Word at position i, not ith word in the dictionary!

SLIDE 18

Example: Spam Filtering

18

Model:
What are the parameters?
Where do these tables come from?

p C,W

1Wn

( ) = p C ( )

p Wi |C

( )

i

∏

ham 0.66 spam 0.33

( )

p Y

( )

|spam p W

the 0.0156 to 0.0153 and 0.0115

f

0.0095 you 0.0093 a 0.0086 with 0.0080 from 0.0075 …

( )

| ham p W

the 0.0210 to 0.0133

f

0.0119 2002 0.0110 with 0.0108 from 0.0107 and 0.0105 a 0.0100 …

SLIDE 19

Word p(w|spam) p(w|ham) Σ log p(w|spam) Σ log p(w|ham) (prior) 0.33333 0.66666

1.1
0.4

Gary 0.00002 0.00021

11.8
8.9

would 0.00069 0.00084

19.1
16.0

you 0.00881 0.00304

23.8
21.8

like 0.00086 0.00083

30.9
28.9

to 0.01517 0.01339

35.1
33.2

lose 0.00008 0.00002

44.5
44.0

weight 0.00016 0.00002

53.3
55.0

while 0.00027 0.00027

61.5
63.2

you 0.00881 0.00304

66.2
69.0

sleep 0.00006 0.00001

76.0
80.5

19

Spam example

SLIDE 20

Problem with this approach

20

2 wins!!

SLIDE 21

Another example

21

Posteriors determined by relative probabilities

(odds ratios):

( ) ( )

| ham | spam p W p W

south-west inf nation inf morally inf nicely inf extent inf seriously inf …

( ) ( )

| am | am p W sp p W h

screens inf minute inf guaranteed inf $205.00 inf delivery inf signature inf …

What went wrong here?

SLIDE 22

Generalization and Overfitting

22

Relative frequency parameters will overfit the training data!
Just because we never saw a 3 with pixel (15,15) on during training

doesn’t mean we won’t see it at test time

Unlikely that every occurrence of “minute” is 100% spam
Unlikely that every occurrence of “seriously” is 100% spam
What about all the words that don’t occur in the training set at all?
In general, we can’t go around giving unseen events zero probability
As an extreme case, imagine using the entire email as the
nly feature
Would get the training data perfect (if deterministic labeling)
Wouldn’t generalize at all
Just making the bag-of-words assumption gives us some

generalization, but isn’t enough

To generalize better: we need to smooth or regularize the

estimates

SLIDE 23

Estimation: Smoothing

23

Maximum likelihood estimates:
Problems with maximum likelihood estimates:
If I flip a coin once, and it’s heads, what’s the estimate for

p(heads)?

What if I flip 10 times with 8 heads?
What if I flip 10M times with 8M heads?
Basic idea:
We have some prior expectation about parameters (here,

the probability of heads)

Given little evidence, we should skew towards our prior
Given a lot of evidence, we should listen to the data

( ) ( )

count total samples

ML

x p x =

( )

1 3

ML

p r =

SLIDE 24

Estimation: Laplace Smoothing

24

Laplace’s estimate (extended):
Pretend you saw every outcome k

extra times

What’s Laplace with k=0?
k is the strength of the prior
Laplace for conditionals:
Smooth each condition

independently:

( ) ( ) ( )

,0 ,1 ,100 LAP LAP LAP

p X p X p X = = = pLAP,k x

( )=

c x

( )+ k

N + k X

( ) ( ) ( )

,

, | =

LAP k

c x y k p x y c y k X + +

SLIDE 25

Estimation: Linear Smoothing

25

In practice, Laplace often performs poorly for p(X|Y):
When |X| is very large
When |Y| is very large
Another option: linear interpolation
Also get p(X) from the data
Make sure the estimate of p(X|Y) isn’t too different from

p(X)

What if α is 0? 1?

pLIN x | y

( )=α p

 x | y

( )+ 1−α ( ) p

 x

( )

SLIDE 26

Real NB: Smoothing

26

For real classification problems, smoothing is critical
New odds ratios:

( ) ( )

| ham | spam p W p W

helvetica 11.4 seems 10.8 group 10.2 ago 8.4 area 8.3 …

( ) ( )

| am | am p W sp p W h

verdana 28.8 Credit 28.4 ORDER 27.2 <FONT> 26.9 money 26.5 …

Do these make more sense?

SLIDE 27

Tuning on Held-Out Data

27

Now we’ve got two kinds of

unknowns

Parameters: the probabilities p(Y|X), p(Y)
Hyperparameters, like the amount of

smoothing to do: k,α

Where to learn?
Learn parameters from training data
Must tune hyperparameters on different

data

Why?
For each value of the hyperparameters,

train and test on the held-out data

Choose the best value and do a final test
n the test data

SLIDE 28

Errors, and What to Do

28

Examples of errors

SLIDE 29

What to Do About Errors?

29

Need more features- words aren’t enough!
Have you emailed the sender before?
Have 1K other people just gotten the same email?
Is the sending information consistent?
Is the email in ALL CAPS?
Do inline URLs point where they say they point?
Does the email address you by (your) name?
Can add these information sources as new

variables in the NB model

Next class we’ll talk about classifiers that let you

add arbitrary features more easily