7-Speech Recognition (Cont’d)
HMM Calculating Approaches
Neural Components
Three Basic HMM Problems
Viterbi Algorithm
State Duration Modeling
Training In HMM
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Speech Recognition Concepts
Speech recognition is inverse of Speech Synthesis
Speech Synthesis
Text
Speech Speech
Phone
Processing Sequence
NLP
NLP
Speech
Processing
Text
Speech
Understanding
Speech Recognition
2
Speech Recognition
Approaches
Bottom-Up Approach
Top-Down Approach
Blackboard Approach
3
Bottom-Up Approach
Signal Processing
Knowledge Sources
Feature Extraction
Voiced/Unvoiced/Silence
Segmentation
Signal Processing
Sound Classification Rules
Feature Extraction
Phonotactic Rules
Segmentation
Lexical Access
Language Model
Segmentation
Recognized Utterance
4
Top-Down Approach
Inventory
Word
of speech Dictionary Grammar
recognition
units
Feature
Analysis
Syntactic
Hypo
thesis
Unit
Matching
System
Lexical
Hypo
thesis
Utterance
Verifier/
Matcher
Recognized Utterance
Task
Model
Semantic
Hypo
thesis
5
Blackboard Approach
Acoustic
Processes
Environmental
Processes
Lexical
Processes
Black
board
Semantic
Processes
Syntactic
Processes
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top down
An overall
view of a
speech
recognition
system
bottom up
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From Ladefoged 2001
Recognition Theories
Articulatory Based Recognition
– Use from Articulatory system for recognition
– This theory is the most successful until now
Auditory Based Recognition
– Use from Auditory system for recognition
Hybrid Based Recognition
– Is a hybrid from the above theories
Motor Theory
– Model the intended gesture of speaker
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Recognition Problem
We have the sequence of acoustic
symbols and we want to find the words
that expressed by speaker
Solution : Finding the most probable
word sequence having Acoustic symbols
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Recognition Problem
A : Acoustic Symbols
W : Word Sequence
we should find
ŵ
so that
P(wˆ | A)  max P(w | A)
w
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Bayse Rule
P( x | y) P( y)  P( x, y)
P( y | x) P( x)
P( x | y ) 
P( y )
P( A | w) P( w)
 P( w | A) 
P( A)
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Bayse Rule (Cont’d)
P(wˆ | A)  max P(w | A)
w
P( A | w) P( w)
 max
w
P( A)
ˆ  Arg max P( w | A)
w
w
 Arg max P( A | w) P( w)
w
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Simple Language Model
w  w1w2 w3  wn
n
P( w)   P( wi | wi 1wi 2  w1 )
i 1
Computing this probability is very difficult and we
need a very big database. So we use from Trigram
and Bigram models.
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Simple Language Model
(Cont’d)
n
Trigram :
P( w)   P( wi | wi 1wi 2 )
i 1
n
Bigram :
P( w)   P(wi | wi 1 )
i 1
n
Monogram :
P( w)   P( wi )
i 1
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Simple Language Model
(Cont’d)
Computing Method :
P(w3 | w2 w1 ) 
Number of happening W3 after W1W2
Total number of happening W1W2
AdHoc Method :
P(w3 | w2 w1 )  1 f (w3 | w2 w1 )  2 f (w3 | w2 )  3 f (w3 )
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7-Speech Recognition
Speech Recognition Concepts
Speech Recognition Approaches
Recognition Theories
Bayse Rule
Simple Language Model
P(A|W) Network Types
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From Ladefoged 2001
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P(A|W) Computing
Approaches
Dynamic Time Warping (DTW)
Hidden Markov Model (HMM)
Artificial Neural Network (ANN)
Hybrid Systems
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Dynamic Time Warping
Method (DTW)
To obtain a global distance between two speech
patterns a time alignment must be performed
Ex :
A time alignment
path between a
template pattern
“SPEECH” and a
noisy input
“SsPEEhH”
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Recognition Tasks
Isolated Word Recognition (IWR) And
Continuous Speech Recognition (CSR)
Speaker Dependent And Speaker
Independent
Vocabulary Size
– Small
<20
– Medium
>100 , <1000
– Large
>1000, <10000
– Very Large >10000
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Error Production Factor
Prosody (Recognition should be
Prosody Independent)
Noise (Noise should be prevented)
Spontaneous Speech
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Artificial Neural Network
x0
x1 .w1
.
.
xN 1
w0

y
N 1
y  ( wi xi   )
i 1
wN 1
Simple Computation Element
of a Neural Network
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Artificial Neural Network
(Cont’d)
Neural Network Types
– Perceptron
– Time Delay
– Time Delay Neural Network Computational
Element (TDNN)
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Artificial Neural Network
(Cont’d)
Single Layer Perceptron
y0
x0
...
...
yM 1
xN 1
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Artificial Neural Network
(Cont’d)
Three Layer Perceptron
...
...
...
...
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Hybrid Methods
Hybrid Neural Network and Matched Filter For
Recognition
Acoustic
Output Units
Speech
Features
Delays
PATTERN
CLASSIFIER
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Neural Network Properties
The system is simple, But too much
iterative
Doesn’t determine a specific structure
Regardless of simplicity, the results are
good
Training size is large, so training should be
offline
Accuracy is relatively good
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Hidden Markov Model
Si
aij
a ji
Sj
Observation : O1,O2, . . .
O1 , O2 , O3 ,, Ot
States in time : q1, q2, . .
.
q1 , q2 , q3 , , qt
All states : s1, s2, . . .
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