1. Sharat.S.Chikkerur S.Anand Mantravadi Rajeev.K.Srinivasan
Speaker Recognition
Sharat.S.Chikkerur
S.Anand Mantravadi Rajeev.K.Srinivasan

2. Speaker Identification
Speaker Recognition
Definition
It is the method of recognizing a person based on his voice
It is one of the forms of biometric identification
Depends of speaker dependent characteristics.
Speaker Recognition
Speaker Identification
Speaker Verification
Speaker Detection
Text
Dependent
Independent
EE 516 Term Project, Fall 2003

3. Speech production Speech production mechanism Speech production model
Impulse
Train
Generator
Glottal Pulse
Model
G(z)
Vocal Tract
V(z)
Radiation
R(z)
Noise source
Pitch Av AN
Speech production mechanism
Speech production model
EE 516 Term Project, Fall 2003

4. Generic Speaker Recognition System
Speech signal
Score
Analysis Frames
Feature Vector
Preprocessing
Feature
Extraction
Pattern
Matching
Verification
Preprocessing
Feature
Extraction
Speaker Model
Enrollment
Stochastic Models
GMM
HMM
Template Models
DTW
Distance Measures
LAR
Cepstrum
LPCC
MFCC
A/D Conversion
End point detection
Pre-emphasis filter
Segmentation
Choice of features
Differentiating factors b/w speakers include vocal tract shape and behavioral traits
Features should have high inter-speaker and low intra speaker variation
EE 516 Term Project, Fall 2003

5. Our Approach Silence Removal Cepstrum Coefficients
Cepstral Normalization
Long time average
Polynomial Function
Expansion
Dynamic Time Warping
Distance Computation
Reference Template
Preprocessing
Feature Extraction
Speaker model
Matching
EE 516 Term Project, Fall 2003

6. Silence Removal Preprocessing Feature Extraction Speaker model
Matching
EE 516 Term Project, Fall 2003

7. Pre-emphasis Preprocessing Feature Extraction Speaker model Matching
EE 516 Term Project, Fall 2003

8. Segmentation Preprocessing Feature Extraction Speaker model Matching
Short time analysis
The speech signal is segmented into overlapping ‘Analysis Frames’
The speech signal is assumed to be stationary within this frame
Q31
Q32
Q33
Q34
EE 516 Term Project, Fall 2003

9. Feature Representation
Preprocessing
Feature Extraction
Speaker model
Matching
Speech signal and spectrum of two users uttering ‘ONE’
EE 516 Term Project, Fall 2003

10. Smoothened Signal Spectrum
Vocal Tract modeling
Preprocessing
Feature Extraction
Speaker model
Matching
Signal Spectrum
Smoothened Signal Spectrum
The smoothened spectrum indciates the locations of the formants of each user
The smoothened spectrum is obtained by cepstral coefficients
EE 516 Term Project, Fall 2003

11. Cepstral coefficients
P[n]
G(z)
V(z)
R(z)
u[n]
Pitch Av AN
Preprocessing
Feature Extraction
Speaker model
Matching
D[]
L[]
D-1[]
x1[n]*x2[n]
x1‘[n]+x2‘[n]
y1‘[n]+y2‘[n]
y1[n]*y2[n]
DFT[]
LOG[]
IDFT[]
x1[n]*x2[n]
X1(z)X2(z)
x1‘[n]+x2‘[n]
log(X1(z)) + log(X2(z))
EE 516 Term Project, Fall 2003

12. Speaker Model F1 = [a1…a10,b1…b10] F2 = [a1…a10,b1…b10] …………….
FN = [a1…a10,b1…b10]
…………….
EE 516 Term Project, Fall 2003

13. Dynamic Time Warping Preprocessing Feature Extraction Speaker model
Matching
The DTW warping path in the n-by-m matrix is the path which has minimum average cumulative cost. The unmarked area is the constrain that path is allowed to go.
EE 516 Term Project, Fall 2003

14. Results Distances are normalized w.r.t. length of the speech signal
Intra speaker distance less than inter speaker distance
Distance matrix is symmetric
EE 516 Term Project, Fall 2003

15. Matlab Implementation
EE 516 Term Project, Fall 2003

16. THANK YOU