1) The document presents a literature review and proposal for a speaker verification system using voice passwords under guidance of Dr. G. Pradhan at NIT Patna. 2) The goals are to develop a text-independent speaker verification system that can perform well with short speech samples in limited data conditions by modeling speaker information and reducing phonetic variability. 3) Baseline experiments use MFCC features with GMM modeling, achieving better performance with more Gaussian mixtures; future work will explore other features and modeling techniques to improve robustness to mismatches.

1. *
Under guidance of
Dr. G. Pradhan
NIT PATNA (ECE dept.)
NAME-PAMMI KUMARI
M.TECH 2nd yr (ECE dept.)
ROLL NO.-1329005

2. • Introduction
• Summary of Literature review
• Issues in existing speaker verification systems
• Motivation for the present work
• Baseline speaker verification system
• Experimental results
• Proposal for future work
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3. To develop voice password based speaker verification
To study impact of text-mismatch on the performance of voice
password based speaker verification system
Develop a voice password based speaker verification system in
text-independent mode
Explore method to model speaker information in limited data
condition
 Most of the application where speech signal of short duration
used around 3-5ms, but Speaker verification system provide
poor performance for short duration speech signal
This degradation of performance is due to phonetic variability
between training and testing speech data

4. SPEAKER VERIFICATION: The speaker verification is a
process of verifying the identity of the claimant . It performs
one-to-one comparison between a newly input voiceprint and the
voiceprint for the claimed identity that is stored in the database.
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Fig :-Block diagram of speaker
verification system
Input
Speech Similarity
Feature
Extraction
Verificatio
n result
Speaker
ID(#M)
Reference
model
(Speaker
#M)
Threshold
Decision

5. *
Training Reference model
Speech
Identity claim
Testing
Speech R
Accept/reject
Pre-
processing
Feature
extraction
Model
Building
Pre-
processing
Feature
extraction comparison
Decision
logic
Fig: Voice password speaker verification system

6. Cont….
• when an identity claim is made by a speaker, the
speech data is compared with respect to the model of
the speaker whose identity is claimed.
• The concept of threshold is used to come up with the
decision.
• If the similarity of the test speech data to the target
model is below the threshold ,the speaker is accepted.
• This process involves a binary decision (accept/reject)
about the claimed identity regardless of the population
size.
• Hence, the performance of the verification system
does not depend on the size of the population.

7. • In the first stage, pre-processing and feature
extraction is performed over a database of
speakers.
• The second stage is to generate models, where
vectors representing speaker specific
characteristic are obtained, this leads to the
feature vectors.
• The third stage is decision, which accepts or
rejects the claimed identity of a speaker.
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8. Basic block diagram of a biometric system
PRE-
PROCESSING
FEATHER
EXTRACTION
APPLICATION
DEVICE
TEMPLATE
GENERATOR MATCHER
STORED
TEMPLATE
SENSOR

9. *
Text-dependent speaker verification-In this, speaker
system is based on the utterance of a fixed
predetermined phrases.
Text-independent speaker verification-In this, the reference
(what are spoken in training) & the test (what are uttered in
actual use) utterance may have completely different content
is text-independent.

10. *Literature
• Research in the field of speaker recognition was initially
carried out in 1950s in Bell laboratories using isolated digites
[1].
• In 2000 most of the research was describe the major elements
of Gaussian mixture model (GMM)-based speaker verification
system used successfully in several NIST Speaker Recognition
Evaluations(SREs).
• 1960-1990 most of the research was focused on extraction of
speaker specific information from the speech data, and
development of text dependent speaker verification system.

11. • In 1990-2005 the speaker recognition method
shifted from template based pattern matching to
statistical modeling. Different statistical
modeling method like GMM and GMM-UBM are
proposed.
• 2005- 2014 most of the research was focused on
compensation of mismatches and development of
practical verification systems. Different
compensation methods like i-vectors and PLDA
are proposed
1. K. H. Davis, et. al., “Automatic recognition of spoken digits,”
J.A.S.A., 24 (6), pp. 637-642, 1952.
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12. • In the speech analysis stage, through the
techniques have been developed to improve the
speaker verification performance, no particular
analysis techniques is specially meant for limited
data condition.
• The use of segmental analysis under limited data
condition provides few feature vectors which
leads to poor speaker models leads to degradation
of performance.
*

13. • Most of the application where speech signal of short
duration used around 3-5ms, but Speaker verification
system provide poor performance for short duration
speech signal
• This degradation of performance is due to phonetic
variability between training and testing speech data
• The phonetic variability may be reduced by artificially
generating multiple utterance.
• Most of the SV system develop score normalization
using on cohort centric normalization. The speaker
centric score normalization may provide better result.
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14. • For Baseline speaker verification the
following parameter are used
 VAD threshold is taken 0.1 of average
energy
 Baseline uses MFCC features
 Feature vector: It uses 39 dimension
feature vector and 20ms frame size with
shift 2ms.
 Modeling: GMM
 GMM size: 8, 16, 32, 64.
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15. *
34.613
32.87
32.0971
32.4634

16. *
27.4725 25.1374
23.6722
22.6190

17. • Extraction of feature to reduce the impact
of phonetic variability.
• Different residue of behavioral feature may
be extracted in addition to MFCC for
speaker verification.
• In this project we considered GMM
modeling technique in next work many
other technique may be used like i-vector.
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18. *