Describes the algorithms for speech based password authentication system . Presentation is not complete but will be updated soon.

1. DEVELOPMENT OF SPEECH BASED
PERSON AUTHENTICATION SYSTEM IN
FPGA
MENTOR:- DR. G.PRADHAN
RAJESH ROSHAN(1204016)
YATENDRA MEENA(1204083)
VINIT KUMAR(1204033)

2. CONTENT
• MOTIVATION
• INRODUCTION
• PROGRESS
• WORKS TO BE DONE IN FUTURE
• CONCLUSION
• REFERENCES

3. MOTIVATION
• DEVELOPMENT OF LOW COMPLEXITY AND LOW COST BIOMETRIC BASED
PASSWORD AUTHENTICATION SYSTEM.
• PRESENT SYSTEMS ARE TOO COSTLY AND COMPLEX.
• DO NOT NEED ANY SPECIAL SETUP AT USER SIDE.

4. INTRODUCTION
• SPEAKER VERIFICATION IS A TASK OF VALIDATING IDENTITY CLAIM OF A
PERSON FROM HIS/HER VOICE.
• VOICE PASSWORD BASED SPEAKER VERIFICATION SYSTEM
• SPEAKER IS FREE TO CHOOSE HIS/HER PASSWORD
• PASSWORD REMAINS SAME FOR TRAINING AND VERIFICATION

5. BLOCK DIAGRAM OF BIOMETRIC SYSTEMS

6. FEATURE EXTRACTION
 THE SPEECH SIGNAL ALONG WITH SPEAKER INFORMATION CONTAINS MANY OTHER
REDUNDANT INFORMATION LIKE RECORDING SENSOR, CHANNEL, ENVIRONMENT ETC.
 THE SPEAKER SPECIFIC INFORMATION IN THE SPEECH SIGNAL[2]
 UNIQUE SPEECH PRODUCTION SYSTEM
 PHYSIOLOGICAL
 BEHAVIORAL ASPECTS
 FEATURE EXTRACTION MODULE TRANSFORMS SPEECH TO A SET OF FEATURE
VECTORS OF REDUCE DIMENSIONS
 TO ENHANCE SPEAKER SPECIFIC INFORMATION
 SUPPRESS REDUNDANT INFORMATION.

7. SELECTION OF FEATURE
• ROBUST AGAINST NOISE AND DISTORTION
• OCCUR FREQUENTLY AND NATURALLY IN SPEECH
• BE EASY TO MEASURE FROM SPEECH SIGNAL
• BE DIFFICULT TO IMPERSONATE/MIMIC
• NOT BE AFFECTED BY THE SPEAKER’S HEALTH OR LONG TERM VARIATIONS IN
VOICE

8. FEATURE EXTRACTION TECHNIQUES
A WIDE RANGE OF APPROACHES MAY BE USED TO PARAMETRICALLY REPRESENT THE SPEECH
SIGNAL TO BE USED IN THE SPEAKER RECOGNITION ACTIVITY.
 LINEAR PREDICTION CODING
 LINEAR PREDICTIVE CEPTRAL COEFFICIENTS
 MEL FREQUENCY CEPTRAL COEFFICIENTS
 PERCEPTUAL LINEAR PREDICTION
 NEURAL PREDICTIVE CODING
MOST OF THE STATE-OF-THE-ART SPEAKER VERIFICATION SYSTEMS USE MEL-FREQUENCY
CEPSTRAL COEFFICIENT (MFCC) APPENDED TO IT’S FIRST AND SECOND ORDER DERIVATIVE AS THE
FEATURE VECTORS
 EASY TO EXTRACT
 PROVIDES BEST PERFORMANCE COMPARED TO OTHER FEATURES
 MFCC MOSTLY CONTAINS INFORMATION ABOUT THE RESONANCE STRUCTURE OF THE VOCAL
TRACT SYSTEM

9. PROGRESS
SPEECH
SIGNAL
FRAMMIN
G
WINDOWIN
G
FFT
MEL-
FREQUENC
Y
WRAPPING
MFCC

10. • STEP 1:- ANALOG TO DIGITAL CONVERSION: IS TRANSFORMED TO DIGITAL
FORM BY SAMPLING IT AT GIVEN FREQUENCY.
SIGNAL AQUASITION

11. SIGNAL

12. FRAMING
• STEP 2:- PRE-EMPHASIS: THE AMOUNT OF ENERGY PRESENT IN THE HIGH
FREQUENCY (IMPORTANT FOR SPEECH) ARE BOOSTED.

13. FRAMING
SPEECH SIGNAL
MAGNIFIED

14. FRAMMING
ONE FRAME OF SPEECH SIGNAL

15. FRAMING
OVERLAPPING
PORTION OF TWO
FRAMES

16. FRAMMING
SPEECH SIGNAL
FRAME OUT

17. WINDOWING
• STEP 3:(FRAMING)THE SIGNAL IS DIVIDED INTO FRAMES OF GIVEN SIZE.

18. WINDOWING
• THE NEXT STEP IS TO WINDOW INDIVIDUAL FRAME TO MINIMIZE THE SIGNAL
DISCONTINUITIES AT THE BEGINNING AND END OF EACH FRAME.
• THE CONCEPT APPLIED HERE IS TO MINIMIZE THE SPECTRAL DISTORTION BY
USING THE WINDOW TO TAPER THE SIGNAL TO ZERO AT THE BEGINNING AND
END OF EACH FRAME.
• WE HAVE USED HAMMING WINDOW

19. WINDOWING
SPEECH SIGNAL HAMMING WINDOW WINDOWED SIGNAL

20. FFT
FFT
Step 4 - Fast Fourier transform: spectral analysis of the signal for each
time frame (decomposition into component frequencies)

21. FFT

22. MFCC
N.I.T. PATNA ECE, DEPTT.
FFT
FFT

23. MFCC
.
DCT
FFT
DISCRETE FOURIER TRANSFORM

24. MEL FREQUENCY WRAPPING
Fmel = (1000/log (2)) * log (1+ f/1000)

25. MFCC
CEPES = DCT( log (abs( FFT(Ywindowed)))
MFC COFFICIENTS IN TIME DOMAINORIGINAL SPEECH SIGNAL

26. MFCC
FFT
DCT

27. MFCC

28. WORK TO BE DONE NEXT
• SPEAKER MODELING USING GMM.
• TESTING AND VERIFICATION.
• FPGA IMPLEMENTATION.

29. CONCLUSION
• WE HAVE SUCCESSFULLY IMPLEMENTED FEATURE EXTRACTION AND
DATABASE CREATION AND ARE WORKING ON MODELLING OF THE FEATURES
EXTRACTED USING GMM TECHNIQUE.
• PARALLELY WE ARE EXPLORING FPGA BOARDES IN WHICH WE CAN
IMPLEMENT ONCE THE ALOGRITM IS EFFECTIVELY OPTIMISED IN MATLAB.

30. REFERENCES
• CAMPBELL, J.P., JR.; "SPEAKER RECOGNITION: A TUTORIAL" PROCEEDINGS OF THE IEEE VOLUME
85,
ISSUE 9, SEPT. 1997 PAGE(S):1437 - 1462.
• SEDDIK, H.; RAHMOUNI, A.; SAMADHI, M.; "TEXT INDEPENDENT SPEAKER RECOGNITION USING THE
MEL FREQUENCY CEPSTRAL COEFFICIENTS" FIRST INTERNATIONAL SYMPOSIUM ON CONTROL,
COMMUNICATIONS AND SIGNAL PROCESSING, PROCEEDINGS OF IEEE 2004
PAGE(S):631 - 634.
• CHILDERS, D.G.; SKINNER, D.P.; KEMERAIT, R.C.; "THE CEPSTRUM: A GUIDE TO PROCESSING"
PROCEEDINGS OF THE IEEE VOLUME 65, ISSUE 10, OCT. 1977 PAGE(S):1428 - 1443.
• ROUCOS, S. BEROUTI, M. BOLT, BERANEK AND NEWMAN, INC., CAMBRIDGE, MA; "THE
APPLICATION OF PROBABILITY DENSITY ESTIMATION TO TEXT-INDEPENDENT SPEAKER
IDENTIFICATION" IEEE
INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, ICASSP '82.
VOLUME:
7, ON PAGE(S): 1649- 1652. PUBLICATION DATE: MAY 1982.

31. THANK
YOU