Voice Activated Un-Lock Technology

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Voice Activated Un-Lock Technology

• V.A.U.L.T
• A Matlab based Simulation

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By

• Siddharth Advani B2213401
• Anand Gokhale B2213420
• Vishal Jain B2213426
• Guided by
• Dr. P.M. Patil

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OBJECTIVE

• Correct decision on a speakers identity claim
  given a speech segment (password)

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MOTIVATION

• Speech contains speaker specific characteristics
• Voiceprint as a biometric (distinguishing trait)
• Natural economical way of identification

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DEFINATIONS

• Client speaker registered on the system
• Impostor speaker who claims a false identity

Mel-filtering a frequency scaling that takes
into account the fact that the ear is sensitive
to linear changes in frequency below 1000 Hz and
logarithmic change in frequency above 1000 Hz
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What is Simulation?

• A simulation is the imitation of the operation of
  a real world process or system over
  time.
• Using MATLAB as a tool, VAULT aims at simulating
  a voice recognition system

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Software Implementation
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MATLAB

• Features
• Interpreter ? Meant for simulation in RD
• High performance numerical computation
• Signal Processing Toolbox

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Visual Basic

• Features
• Easy to implement.
• Very user friendly, interactive.
• Compatible with MATLAB and any Windows version.
• Less complicated than the GUI of MATLAB.
• Any Microsoft application can be embedded in the
  VB.

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Zones Of VAULT
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Phase 1 - Identification
FEATURE EXTRACTION
PATTERN RECOGNITION
USER ID
WORD
SYSTEM DATABASE
TRAINING
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PHASE 1 - IDENTIFICATION
THE WORD IS DIVIDED INTO SEGMENTS 256 SAMPLES IN
EACH SEGMENT
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PHASE 1 - IDENTIFICATION
PROCESS
FEATURE EXTRACTION
VECTOR QUANTIZER
DECISION
WORD
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PHASE 1 - IDENTIFICATION
CEPSTRUM COEFFICIENTS ARE QUANTIZED USING A
CODEBOOK OF 128 VECTORS
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PHASE 1 - IDENTIFICATION
1
2
CLIENT
3
3
DISTANCE12
4
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Database
Identification
EVERY SPEAKER IS GIVEN A TAG
Zero
4
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PHASE 2 - Authentication
ACCEPT REJECT
PATTERN RECOGNITION
FEATURE EXTRACTION
PASS-WORD
SYSTEM DATABASE
TRAINING
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PHASE 2 - AUTHENTICATION
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PHASE 2 - AUTHENTICATION
USER
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PHASE 2 - AUTHENTICATION
THRESHOLD
PASSWORD
DECISION
ACCEPT/ REJECT
DISTANCE
USER
CLIENT
THRESHOLD DECIDES THE DECISION
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Main Obstacle

• How to define and extract the unique features of
  human voice
• CEPSTRUM
• cepstrum(frame)IDFT(log(DFT(frame)))

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PATTERN MATCHING
Dynamic Time Warping Vector Quantization Nearest
Neighbour
Hidden Markov Model Gaussian Mixture Model
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VECTOR QUANTIZATION

• Goal finding how the data is clustered
• A (feature) vector space is broken into cells
• Speaker model codebook
• Codebook set of prototype vectors (codevectors)
• Codevector vector computed from "similar" single
  (feature) vectors (e.g. 8 cepstrum coefficients
  makes 1 codevector)

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CLUSTERING
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RESULTS
THRESHOLD 5
REJECT
ACCEPT
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PERFORMANCE EVALUATION

• False Rejection (FR)
• A client request as himself/herself is rejected
• False Acceptance (FA)
• An impostor request as a client is accepted
• Genuine Acceptance (GA)
• A client request as himself/herself is accepted

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ACCURACY

• FAR (False Acceptance Rate) Prob. of false
  acceptance
• Estimate false acceptances
• 
  ----------------------------------------
• 
  false claims
• FRR (False Rejection Rate) Prob. of false
  rejection
• Estimate false rejections
• 
  ----------------------------------------
• 
  true claims
• GAR (Genuine Acceptance Rate) Prob. of genuine
  acceptance
• Estimate true acceptances
• 
  ----------------------------------------
• 
  true claims

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GRAPHS
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THRESHOLD

• The threshold T can be determined by
• 1) choosing T to satisfy a fixed FA or FR
  criterion
• 2) varying T to find different FA/FR ratios and
  choosing T to give the desired FA/FR ratio.

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SOURCES OF ERROR

• CLIENT
• Bad Pronunciation
• Extreme emotional states (e.g. stress)
• Sickness (head colds alter the vocal tract)
• Aging (vocal tract can drift away from models
  with age)
• Channel mismatch (using different microphones for
  enrollment and verification)
• IMPOSTER
• Mimicry
• AMBIENT NOISE

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STRENGTHS WEAKNESSES
Strengths

• SPEECH IS EASY TO PRODUCE
• LOW COMPUTATION REQUIREMENTS
• SPEECH IS A BEHAVIORAL SIGNAL
• SPOOFING OF SYSTEMS

Weaknesses
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APPLICATIONS

• Security Systems
• Voice Dialing
• Access control to computers / databases
• Remote access to computer networks
• Electronic commerce
• Forensic
• Telephone banking

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Hardware Application
Robotics

• Aim To control a robot via voice

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Robot Control via Voice
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Parallel Port Interface

• 25 pin D-type Male Connector
• Parallel port of computer 3 registers
• Data register
• Status register
• Control register

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FM Transmitter-Receiver

• Frequency of operation 433.92 MHz
• Modulation type ASK
• Bandwidth 200 kHz

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FEA The Robot

• Features
• Wireless
• Prime Mover DC motors

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Relay Driver IC ULN2803

• Eight Darlington Arrays
• Internal Free Wheeling Diodes
• Output Compatible with TTL logic

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FEAs Drivers IC L293B Motor Driver
IC

• Four Channel drivers
• Bidirectional Motor drive
• High voltage , high current output

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PROJECT TIME DISTRIBUTION

• JAN PARTICIPATED AT IIT TECHFEST
• FEB (a) SUBMITTED PAPER AT TECHKRITI KANPUR
• (b) MADE FEA FOR FERVOR AT COEP
• (c) MATLAB VISUAL BASIC TRAINING
• MAR PHASE 1 2 COMPLETED IN MATLAB
• APR MATLAB VISUAL BASIC INTERFACE
• MAY EVALUATION OF SOFTWARE FAR,FRR GAR
• JUNE APPLICATION BOARD

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Future Expansion

• Implementation over the DSP board
• Making the system to work in real time
• Speech Recognition