1. A PRESENTATION BY SHAMALEE DESHPANDE
SPEAKER RECOGNITION
A PRESENTATION BY
SHAMALEE DESHPANDE

2. INTRODUCTION Speaker Recognition * Automatically recognizing speaker
* Uses individual information from the speaker’s speech waves

3. INTRODUCTION Two Approaches Text-Dependant Recognition
Text-Independent Recognition

4. INTRODUCTION Two Approaches Text-Dependant Recognition
*Use of keywords or sentences having the same text for the templates and the recognition
Text-Independent Recognition

5. INTRODUCTION Text-Dependant Recognition Text-Independent Recognition
Two Approaches
Text-Dependant Recognition
Text-Independent Recognition
* Does not rely on a specific text being spoken.

6. INTRODUCTION Classes of Sound: Voiced, unvoiced, Plosive
Production of Pitch Frequency
and Formants
Glottal Waveform

7. BLOCK DIAGRAM OF A SPEAKER RECOGNITION SYSTEM

8. DESIRABLE ATTRIBUTES OF A SPEAKER RECOGNITION SYS
Feature should occur naturally and frequently in speech
Easily measurable
Doesn’t change over time or be affected by speakers health
Isn’t affected by background noise
Not be subject to mimicry

9. SOURCES OF VARIABILITY IN SPEECH
Phonetic Identity
Two samples may correspond to different phonetic segments. E.g. Vowel and fricative
Pitch
Pitch, other features like breathiness and amplitude can be varied
Speaker
Differences due to source physiology, emotions
Microphone
Environment

10. Possible Acoustic Parameters
* Formant Frequencies
* LPC
* Pitch
* Nasal Co articulation
* Gain

11. COMMON SPEAKER RECOGNITION TECHNIQUES
DISCRETE FOURIER TRANSFORM
LINEAR PREDICTIVE CODING
CEPSTRAL ANALYSIS
DYNAMIC TIME WARPING
HIDDEN MARKOV MODELS

12. DISCRETE / FAST FOURIER TRANSFORM
Changes time domain signals into freq domain signal representations
Enables reduced complexity for processor
Read N speech samples from input
Append N-L zeroes to the input data
Calculation of DFT
Windowing

13. LINEAR PREDICTIVE CODING
TUBE
Vocal tract
BUZZER
Glottal excitation
Characterized by intensity and pitch
Characterized by formants
LPC model of the speech producing organs of the body

14. CEPSTRAL ANALYSIS
Dis-adv of DFT/FFT is that formant freqs may shift the pitch or overlap it
In Cepstral analysis, formants are completely removed from the spectrum
Defined as Fourier Transform of the Log of the power spectrum
S(n) = p(n) * v(n)
X(n) = w(n) * s(n)
S’(w) = p’(w) * v’(w) Fourier Transform
Log S’(w)=log p’(w) + log v’(w)
C(q)= log S’(q) = log p’(q) + log v’(q)
Q – quefrency , C(q) – complex cepstrum

15. CEPSTRAL ANALYSIS
Window
DFT
LOG
IDFT
Speech
Cepstrum

16. DYNAMIC TIME WARPING
Incoming speech is usually compared frame by frame with stored template
Achieved via a pair wise comparison of feature vectors from each sequence
Dis Adv – variation in length of corresponding phonemes
DTW takes into account non linear relation between lengths of the two signals
Used as a matching algorithm
Example DTW grid

17. HIDDEN MARKOV MODELS
Speech signal is identified during search process rather than explicitly
Comprises of –
Hidden Markov Chain representing temporal variability
Observable process representing spectral variability
Portrayed as stochastic pair (X,Y)
HMM is a Finite State Machine where a Probability Density Function p(x|s) is associated with each state s

18. FUTURE RESEARCH
To extract and apply all levels and information from the speech signal conveying speaker identity
Acoustic – use spectral features conveying vocal tract information
Prosodic - use features derived from pitch, energy tracks
to classify information
Phonetic – use phone sequences to characterize speaker specific pronunciations
Idiolect – use words to characterize user specific word patterns
Linguistic – use linguistic patterns to characterize speaker specific conversation style

19. APPLICATIONS
Access Control- physical facilities, computer networks and websites
PC Login and Password Reset
Secured Transactions – remote banking and online credit card purchase authentication
Time Attendance - workplaces
Law Enforcement – forensics, parole