1. Ala’a Spaih Abeer Abu-Hantash Directed by Dr.Allam Mousa
Text-Independent
Speaker Identification
System
Ala’a Spaih Abeer Abu-Hantash
Directed by
Dr.Allam Mousa

2. Outline for Today 1. 2. 3. 4. 5. Speaker Recognition Field
System Overview 2.
MFCC & VQ 3.
Experimental Results 4.
Live Demo 5.

3. Speaker Recognition Field
Speaker Verification
Speaker Identification
Text
Dependent
Text
Independent
Text
Dependent
Text
Independent

4. System Overview Speaker modeling Speaker Model Database Feature
Training
mode
Speaker
Model
Database
Speaker
modeling
Feature
extraction
Feature
Matching
Testing
Mode
Speech input
Speaker ID
Decision
Logic

5. Feature Extraction
Feature extraction:is a special form of dimensionality reduction.
The aim: is to extract the formants.

6. Feature Extraction
The extracted features must have specific characteristics:
Easily measurable, occur naturally and frequently in speech.
Not change over time.
Vary as much among speakers, consistent for each speaker.
Not affected by: speaker health, background noise.
Many algorithms to extract them:
LPC,LPCC,HFCC,MFCC.
We used Mel Frequency Cepstral Coefficients algorithm:
MFCC.

7. Feature Extraction Using MFCC
Input speech
Framing and windowing
Fast Fourier transform
Absolute value
Mel scaled-filter bank
Log
Feature vectors
Discrete cosine transform

8. Framing And Windowing
FFT
Spectrum
Glottal
pulse
Vocal tract

9. Mel Scaled-Filter Bank
Spectrum
Mel
spectrum
mel(f)= 2595*log10(1+f/700)

10. Cepstrum Mel spectrum MFCC Coeff.
DCT of the logarithm of the magnitude spectrum,
the glottal pulse and the impulse response can be separated.

11. Classification
Classification, that is to build a unique model for each speaker in the database.
Two major types of models for classification.
Stochastic models:
GMM,HMM,ANN
Template models:
VQ , DTW
We used VQ algorithm.

12. Clustered into codewords
VQ Algorithm
The VQ technique consists of extracting a small number of representative feature vectors.
The first step is to build a speaker-database consisting of N codebooks, one for each speaker in the database.
Clustered into codewords
Speaker model
(codebook)
Speaker
Feature vectors
This done by
K-means
Clustering algorithm

13. K-means Clustering start No. of clusters k No yes centroids No change
End
Distance objects to
centroids
Grouping based on
minimum distance

14. VQ Example
Given data points, split into 4 codebook vectors with initial values at (2,2),(4,6),(6,5),(8,8).

15. VQ Example
Once there’s no more change, the feature space will be partitioned into 4 regions. Any input feature can be classified as belonging to one of the 4 regions. The entire codebook can be specified by the 4 centroid points.

16. K-means Clustering
If we set the codebook size to 8 then the output of the clustering will be: VQ
MFCC’s of a speaker (1000x12)
Speaker Codebook (8x12)

17. Feature Matching For each codebook a distortion measure is computed.
The speaker with the lowest distortion is chosen.
Define the distortion measure Euclidean distance.

18. System Operates In Two Modes
Offline
Online
Monitoring
Microphone
Inputs
MFCC
Feature
Extraction
Calculate VQ
Distortion
Make
Decision &
Display

19. Applications Speaker Recognition for Authentication.
Banking application.
Forensic Speaker Recognition
Proving the identity of a recorded voice can help to convict a criminal or discharge an innocent in court.
Speaker Recognition for Surveillance.
Electronic eavesdropping of telephone and radio conversations.

20. Results
12 MFCC, 29 Filter banks, 64 Codebook size … ELSDSR database.
To show how the system identify the speaker according to Euclidean distance calculation.
Sp 1
Sp 2
Sp 3
Sp 4
Sp 5

21. Results Frame Size Vs. ID rate. No. of MFCC ID Rate 5 76 % 12 91 % 20
Number of MFCC Vs. ID rate.
Frame Size Vs. ID rate.
Frame size(10-30) ms
Good
No. of
MFCC ID
Rate 5
76 % 12
91 % 20
Above 30 ms
Bad

22. Results
The effect of the codebook size on the ID rate & VQ distortion.

23. Results
Number of filter-banks Vs. ID rate & VQ distortion.

24. Results The performance of the system on different test shot lengths.
Test speech length ID
Rate
0.2 sec
60 %
2 sec
85 %
6 sec
90 %
10 sec
95 %

25. Summary Effect of changing some parameters on: MFCC algorithm.
VQ algorithm.
Our system identify the speaker regardless of the language and the text.
Satisfied results:
The same training and testing environment.
Test data needs to be several ten seconds.

26. Thank You