speaker verification for remote authentication
TRANSCRIPT
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MAJOR PROJECT FINAL PRESENTATION :
TEXT PROMPTED REMOTE
SPEAKER AUTHENTICATION
Project Members:
Ganesh Tiwari (75010)
Madhav Pandey(75014)
Manoj Shrestha(75018)
Project Supervisor :
Dr. Subarna Shakya
Associate Professor
Internal Examiner:
Er. Manoj Ghimire
External Examiner
Er. Bimal Acharya
Tribhuvan University
Institute of Engineering
Pulchowk CampusDepartment of Electronics and Computer Engineering
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INTRODUCTION
Voice biometric system
User login
Text-Prompted system Claimant is asked to speak a prompted(random) text
Speech and Speaker Recognition
Why Text prompted ? Playback attack
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OUR SYSTEM
Feature :MFCC
Modeling and Classifications : both statistical
GMM- Speaker Modeling:
HMM/VQ -Speech Modeling:
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PROPERTIESOF SPEECH SIGNAL
Carries both Speech Content and Speaker identity
What makes Speech Signal Unique ?
Each phoneme resonates at its own fundamental frequency
and harmonics of it
Studied over short period : short time spectral analysis
What is Speaker Dependent information
Fundamental frequency, primarily function of the dimensions and tension of the vocal chords
size and shape of the mouth, throat, nose, and teeth
Studied over long period : all the variations from that speaker
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UNIQUENESSIN PHONEME
0 500 1000 1500 2000 2500-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
Samples
Amplitude
Phoneme /ah/
Phoneme /i:/
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Pre-Processing and Feature Extraction
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PREPROCESSING : STEPS
1)Silence Removal
0 1 2 3 4 5 6 7 8 9
x 104
-1
-0.5
0
0.5
1
0 0.5 1 1.5 2 2.5 3 3.5 4
4
-1
-0.5
0
0.5
1
Silence Signal
Silence Removed
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PREPROCESSING :STEPS (CONTD..)
1)Silence Removal
2)Pre-Emphasis
0 2000 4000 6000 8000 10000 120000
0.01
0.02
0.03
0.04
0.05
Frequency (Hz)
|Y(f)|
0 2000 4000 6000 8000 10000 120000
1
2
3
4
5x 10
-3
Frequency (Hz)
|Y(f)|
Boosted highFrequencies
Suppressed high
Frequencies
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1)Silence Removal
2)Pre-Emphasis
3)Framing
50% overlapped, 23ms
PREPROCESSING :STEPS (CONTD..)
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1)Silence Removal
2)Pre-Emphasis
3)Framing
4)Windowing
0 10 20 30 40 50 60
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Hamming Window
0 200 400 600 800 1000 1200-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.04
0 200 400 600 800 1000 1200-0.05
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.04
0.05
PREPROCESSING :STEPS (CONTD..)
Hamming WindowWindowed Signal
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FEATURE EXTRACTION
MFCC : Mel Filter Cepstral Coefficients
Perceptual approach
Human Ear processes audio signal in Mel scale
Mel scale : linear up to 1KHz and logarithmic after
1KHz
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MFCC EXTRACTION: (CONTD..)
Steps :
FFT Mel Filter Log DCT CMS
Mel Filter : 12
Filtering of absolute fft coefficients using triangular filter bank inMel scale
MFCC gives distribution of energy acc. to filters in Melfrequency band
Mel Filter Bank
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EXTRAFEATURES :ENERGYAND DELTAS
For achieving high recognition rate
A Energy Feature
Delta and Delta-Delta
deltavelocity feature
double deltaacceleration feature
Co-articulation
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COMPOSITIONOF FEATURE VECTOR
12 MFCC Features
12 MFCC
12 MFCC
1 Energy Feature
1 Energy
1 Energy
39 Features from each frame
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Speech Recognition/Verification by
HMM/VQ
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HIDDEN MARKOV MODEL (HMM)
HMM is the extension of Markov Process
Markov Process consist of observable states
HMM has hidden states and observable symbols
per states
HMM is the stochastic model
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HMM (CONTD)
Parameters
1) The initial state distribution ()
2) State transition probability distribution (A)
3) Observation symbol probability distribution (B)
The HMM Model
(A,B,)
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EXAMPLE:
PRONUNCIATIONMODELOFWORD TOMATO
(A,B,)
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HMM IMPLEMENTATION
Feature Vector observation symbols , 256
Phonemeshidden states, 6
Left to right HMM
Discrete Hidden Markov Model (DHMM) with
Vector Quantization (VQ) technique
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SPEECH RECOGNITION SYSTEM
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VECTOR QUANTIZATION
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Speaker Recognition/Verification by
GMM
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SPEAKER VERIFICATION SYSTEM
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SPEAKER MODELING (GMM)
Gaussian Mixture Model
Parametric probability density function
Based on soft clustering technique
Mixture of Gaussian components
= (, ,)
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SPEAKER MODEL TRAINING
Estimate the model parameters
Expectation Maximization algorithm
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SPEAKER VERIFICATION
Based on likelihood ratio
=
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TOOLS USED
Languages: Adobe Flex
Java
Blaze DS for RPC
Servers: Apache Tomcat
MySQL
Versioning Tortoise SVN
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OUTPUT : SNAPSHOT (GUI)
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APPLICATION AREAS
Telephone transaction
Telephone credit card purchase,
Telephone stock trading
Access control
Physical facilities
Computer networks
Information retrieval
Customers information
Forensics
Voice sample matching
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LIMITATIONAND FUTURE ENHANCEMENT
Noise reduction
Training on more data
Combine with
other features
other classification methods
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Thanks
Any queries ?