BIOMETRICS

• USES PHYSICAL OR BEHAVIOURAL CHARACTERISTICS

• DIFFERENT TYPES OF BIOMETRICS

• BIOMETRIC SYSTEM

VOICE RECOGNITION

• RECOGNIZES PEOPLE FROM THEIR VOICES

• VOICE IS A UNIQUE CHARACTER TRAIT

• VOICE DEPENDS ON MANY FACTORS

• TYPES OF VOICE RECOGNITION

SPEECH AND SPEAKER RECOGNTION

• SPEECH->WHAT?

• SPEAKER->WHO?

SPEAKER RECOGNITION

Speaker Recognition

Classification –Styles of input

Speaker Identification

Speaker Verification

A technology that verifies a speaker’s identity based on the speaker’s voice.

Technology of determining an unknown speaker's identity.

Classification – Styles of input

Speaker Recognition

Text Independent Text Prompted

Text Dependent

Fixed Phrase Fixed Phrase

Speaker #1 Speaker #n

Speaker #2

...

Feature Extractor

Hypotheses space

Speaker Modeling

Under preparation

Hypotheses space

Hypothesis representation

Desired hypothesis

define

SearchTraining

examples

Best fit?

Markov Models

• Model to capture extracted voice features

• Probablistic process

• state diagrams- states , transitions

• applied in weather forecasts,dna modelling x — hidden states

y — observable outputsa — transition probabilitiesb — output probabilities

• Coin toss method

• 2 coins A,B: head or tail of any one may appear which is a probability

• feature vectors correspond to head /tail

• state corresponds to the coins A/B

Hidden markov models• Observations are probablistic functions

of states: urn and ball model

• main elements of hmm

• {1,2…N}-individual states ,the initial state time being qt

• {v1,v1…vm}-observation symbols

• state -transition probability

• Observation symbol probability distribution

• B = {bik = P(ok | qi)}

• initial state distribution

• Π = {pi = P(qi at t=1)}.

• System is given by:

• F= (A, B, Π).

Baye’s rule

P(A).P(B|A) = P(B).P(A|B)

P(Fj|OT) = P(OT|Fj).P(Fj) P(OT)

Fj speaker model

OT feature vector of test utterance

SPEAKER IDENTIFICATION

The model with maximum probability of P(Fj|OT) is identified as speaker.

Speaker X

Speaker Y

Speaker Z

Speaker with max probability

Identified

Feature vectorsO1,O2,….OT

SPEAKER VERIFICATION

Target model FA

Background models FB

P(FA|OT) P(OT|FA).P(FA)/P(OT)

P(FB|OT) P(OT|FB).P(FB)/P(OT)

taking log

X = log[P(OT|FA)] – log[P(OT|FB)]

Speaker Y

Imposter 1

Imposter 2

Imposter 3

Feature vectorsO1,O2,..

OT

X

X>=0, acceptX<0, reject

Speaker verification

APPLICATIONS OF SPEAKER RECOGNITION

• USED TO SECURE OUR COMPUTER PASSWORDS

• USED IN ATM’S

• POTENTIAL APLLICATIONS

ADVANTAGES OF SPEAKER RECOGNITION

• IN THE WORLD OF COMPUTER AND INTERNET

• IN THE FIELD OF CREDIT CARD,DEBIT CARDS AND ATM

• POTENTIAL COSTUMERS

DISADVANTAGES OF SPEAKER RECOGNITION

• LANGUAGE PROBLEMS

• CAN BE EASILY OPENED BY MIMICRYING

• IN CASE OF TEMPERORY USE WE CANT HAVE THIS SYSTEM

• TECHNICAL AND HARDWARE PROBLEMS