1. Sfax University, Tunisia
National Engineering School of Sfax
Advanced Technologies for Medicine and Signals Research Unit
Efficient parameterization for Automatic speaker recognition using Support Vector Machines
Today I will present my paper entitled
Rania Chakroun
Mondher Frikha
Leila beltaifa zouari

2. 1. 2. 3. 4. Outline Introduction System Overview Experimental Results
The outline of my presentation is orgnized as follows:
**First, I will present the context of this work with an introduction about speaker recognition
**Second, I will give an overview of the systems developped
**Then, I will detail our Experimental Results.
** and Finally, I will give our Conclusions & Future Work
Let us begin with the introduction
Conclusions & Future Work 4.
Chakroun Rania ISDA 2015

3. Motivation
Individuals need to be identified in sensitive domains and applications such as
Access control
Research and supervision individuals
Border control
Military intelligence (suspects' identification) ….
that’s why biometric technology is developed to improve security and reduce fraud
Instead of passwords, PINs or signatures (which can be stolen, forgotten or forged)
Chakroun Rania ISDA 2015

4. Motivation There are many biometric modalities Fingerprint Palmprint
Hand Geometry
Face
Iris
Retina Scan
DNA
Signatures
Voiceprint ….
Which is the modality adopted in this work
Chakroun Rania ISDA 2015

5. What language was spoken?
Extracting Information from Speech
Speech conveys many Information
Where is he/she from?
What language was spoken?
What was spoken?
Accent Recognition
Language Recognition
Speech Recognition
Emotion Recognition
Gender Recognition
Speaker Recognition
Positive? Negative?
Happy? Sad?
Male or Female?
Who spoke?
Chakroun Rania ISDA 2015

6. Speaker Recognition applications
The applications of speaker recognition technology are quite varied
Authentication
Banking application.
Law Enforcemet
Proving the identity of a recorded voice can help to convict a criminal or discharge an innocent in court.
Surveillance.
Electronic eavesdropping of telephone and radio conversations.
Chakroun Rania ISDA 2015

7. Problems Speaker recognition area suffers from many problems
Inter-Speaker Variability
Intra-Speaker Variability
Spoofing attacks
Learning and testing data duration
…etc.
Speaker reco applications share many problems
Chakroun Rania ISDA 2015

8. Objective Determine whom is talking from set of known voices
No identity claim from user
Limited data duration
which information we use ?
how can we exploit them ?
In this work, we want to Determine ……………Knowing that No identity
Chakroun Rania ISDA 2015

9. 1. 2. 3. 4. Outline Introduction System Overview Experimental Results
Let us pass in
Conclusions & Future Work 4.
Chakroun Rania ISDA 2015

10. Speaker Recognition System
Speaker recognition systems have 2 main parts
Which characteristics?
Which approaches?
Feature
Extraction
Learning
algorithm
Speaker Recognition Systems havo 2 main parts
These systems use on which approaches they depend?
the
Feature
Extraction
Recognition
algorithm
Decision
Chakroun Rania ISDA 2015

11. Characteristics & approaches
MFCC
LFCC
LPCC
PLP,… …
Approaches
Gaussian Mixture Models (GMM)
Support Vector Machines (SVM)
We find many Characteristics
We find different Approaches where the most successful are
Chakroun Rania ISDA 2015

12. 1. 2. 3. 4. Outline Introduction System Overview Experimental Results
Conclusions & Future Work 4.
Chakroun Rania ISDA 2015

13. Database TIMIT Database 64 American speakers
From 8 dialect region (« dr1 » to « dr8 »)
With 10 utterances/ speaker
Average duration of 3.28 s /utterance
Dialect Region code
Dialect Region
DR1
New England
DR2
Northern
DR3
North Midland
DR4
South Midland
DR5
Southern
DR6
New York City
DR7
Western
DR8
Army brat
Twenty eight
Chakroun Rania ISDA 2015

14. Speaker Recognition System
Which characteristics we use?
Which approaches we use?
Feature
Extraction
Learning
algorithm
So Which characteristics we use?
And Which approaches we shoose to use?
Feature
Extraction
Recognition
algorithm
Decision
Chakroun Rania ISDA 2015

15. Characteristics & approaches
System based on Support Vector Machine (SVM)
Characteristics
First System based on Mel Frequency Cepstral Coefficients (MFCC)
19 MFCC+Delta+double delta (60-dimensional vectors)
Second proposed system based on reduced MFCC features
12 MFCC+Delta+double delta (39-dimensional vectors)
Third proposed system based new cepstral features
Cepstral Mean and Variance Normalization (CMVN)
39-dimensional vectors
We shoose the approach based
We develop
Chakroun Rania ISDA 2015

16. Results best result 98,43 % best result 100 % best result 100 %
Speaker identification rates with SVM-based systems with 8 utterances for training and 2 utterances for the test using RBF and Linear kernels
best result 98,43 %
best result 100 %
best result 100 %
Chakroun Rania ISDA 2015

17. Results best result 96,88 % best result 98,43 % best result 100 %
Speaker identification rates with SVM-based systems with 3 utterances for training and 2 utterances for the test using RBF and Linear kernels
best result 96,88 %
best result 98,43 %
best result 100 %
Chakroun Rania ISDA 2015

18. 1. 2. 3. 4. Outline Introduction System Overview Experimental Results
Conclusions & Future Work 4.
Chakroun Rania ISDA 2015

19. Conclusions & Future Work
Speaker recognition technology is a viable technique currently available for applications.
A new approach based on SVM using new low-dimensional CMVN feature vectors is proposed and gives significant improvements for Speaker identification.
Future work will focus on further experiments to test the new approach with new algorithms and/or features.
Research will focus on using speaker identification for more unconstrained and uncontrolled situations.
Chakroun Rania ISDA 2015

20. Thank you for your attention