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 Campus

Department of Electronics and Computer Engineering

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

OUR SYSTEM

Feature : MFCC

Modeling and Classifications : both statistical

GMM - Speaker Modeling :

HMM/VQ - Speech Modeling :

PROPERTIES OF 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

UNIQUENESS IN PHONEME

0 500 1000 1500 2000 2500-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Samples

Am

plitu

de

Phoneme /ah/

Phoneme /i:/

Pre-Processing and Feature Extraction

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

x 104

-1

-0.5

0

0.5

1

Silence Signal

Silence Removed

PREPROCESSING :STEPS (CONTD..)

1)Silence Removal2)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 high

Frequencies

Suppressed high

Frequencies

1)Silence Removal2)Pre-Emphasis3)Framing

50% overlapped, 23ms

PREPROCESSING :STEPS (CONTD..)

1)Silence Removal2)Pre-Emphasis3)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

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0.04

0.05

PREPROCESSING :STEPS (CONTD..)

Hamming Window

Windowed Signal

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

MFCC EXTRACTION: (CONTD..)

Steps :

FFT Mel Filter Log DCT CMS

Mel Filter : 12 Filtering of absolute fft coefficients using triangular filter bank in

Mel scale

MFCC gives distribution of energy acc. to filters in Mel frequency band

Mel Filter Bank

EXTRA FEATURES :ENERGY AND DELTAS

For achieving high recognition rate

A Energy Feature

Delta and Delta-Delta

delta velocity feature

double delta acceleration feature

Co-articulation

COMPOSITION OF FEATURE VECTOR

12 MFCC Features

12 Δ MFCC

12 Δ Δ MFCC

1 Energy Feature

1 Δ Energy

1 Δ Δ Energy

39 Features from each frame

Speech Recognition/Verification by

HMM/VQ

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

HMM (CONTD…)

Parameters

1) The initial state distribution (π)

2) State transition probability distribution (A)

3) Observation symbol probability distribution (B)

The HMM Model

(A,B,)

EXAMPLE:

PRONUNCIATION MODEL OF WORD TOMATO

(A,B,)

HMM IMPLEMENTATION

Feature Vector observation symbols , 256

Phonemes hidden states, 6

Left to right HMM

Discrete Hidden Markov Model (DHMM) with

Vector Quantization (VQ) technique

SPEECH RECOGNITION SYSTEM

VECTOR QUANTIZATION

Speaker Recognition/Verification by

GMM

SPEAKER VERIFICATION SYSTEM

SPEAKER MODELING (GMM)

Gaussian Mixture Model

Parametric probability density function

Based on soft clustering technique

Mixture of Gaussian components

= (𝑤𝑚, 𝜇 𝑚 , 𝐶𝑚)

SPEAKER MODEL TRAINING

Estimate the model parameters

Expectation Maximization algorithm

SPEAKER VERIFICATION

Based on likelihood ratio

= 𝑙𝑖𝑘𝑒𝑙𝑖ℎ𝑜𝑜𝑑 𝑆 𝑐𝑜𝑚𝑒𝑠 𝑓𝑟𝑜𝑚 𝑡ℎ𝑒 𝑠𝑝𝑒𝑎𝑘𝑒𝑟′𝑠 𝑚𝑜𝑑𝑒𝑙

𝑙𝑖𝑘𝑒𝑙𝑖ℎ𝑜𝑜𝑑 𝑆 𝑐𝑜𝑚𝑒𝑠 𝑓𝑟𝑜𝑚 𝑖𝑚𝑝𝑜𝑠𝑡𝑒𝑟′𝑠 𝑚𝑜𝑑𝑒𝑙

TOOLS USED

Languages: Adobe Flex

Java

Blaze DS for RPC

Servers: Apache Tomcat

MySQL

Versioning Tortoise SVN

OUTPUT : SNAPSHOT (GUI)

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

LIMITATION AND FUTURE ENHANCEMENT

Noise reduction

Training on more data

Combine with

other features

other classification methods

Thanks

Any queries ?