Transcript BIOMETRICS VOICE RECOGNITION Meaning     Bios : Life Metron : Measure Biometrics are used to identify the input sample when compared to a template, used in cases.

BIOMETRICS
VOICE RECOGNITION
Meaning
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Bios : Life
Metron : Measure
Biometrics are used to identify the input
sample when compared to a template, used in
cases to identify specific people by certain
characteristics.
Possession based
Knowledge based
Characteristics
BIOMETRICS
PHSYIOLOGICAL
BEHAVIORAL
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Physiological are related to the shape of the
body. The oldest traits, that have been used for
more than 100 years, are fingerprints. Other
examples are face recognition, hand geometry
and iris recognition.
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Behavioral are related to the behavior of a person.
The first characteristic to be used, still widely used
today, is the signature. More modern approaches are
the study of keystroke dynamics and of voice
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Strictly speaking, voice is also a physiological trait
because every person has a different pitch, but voice
recognition is mainly based on the study of the way a
person speaks, commonly classified as behavioral.
Introduction
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Speaker recognition has a history dating back
some four decades and uses the acoustic
features of speech that have been found to
differ between individuals.
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There is a difference between speaker
recognition (recognizing who is speaking) and
speech recognition (recognizing what is being
said). These two terms are frequently
confused, as is voice recognition.
Voice recognition is a synonym for speaker,
and thus not speech, recognition. In addition,
there is a difference between the act of
authentication (commonly referred to as
speaker verification or speaker
authentication) and identification.
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If the speaker claims to be of a certain identity
and the voice is used to verify this claim this is
called verification or authentication. On the
other hand, identification is the task of
determining an unknown speaker's identity.
In a sense speaker verification is a 1:1 match
where one speaker's voice is matched to one
template (also called a "voice print") whereas
speaker identification is a 1:N match where the
voice is compared against N templates.
Variants of speaker recognition
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Each speaker recognition system has two phases:
Enrollment and verification.
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ENROLLMENT
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During enrollment, the speaker's voice is recorded
and typically a number of features are extracted to
form a voice print, template, or model.
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Speech Samples are
waveforms
Time on horizontal axis
and Loudness on
vertical axis
Speaker recognition
system analyses
frequency content
Compares
characteristics such as
the quality, duration
intensity dynamic and
pitch of the signal
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. In the verification phase, a speech sample or
"utterance" is compared against a previously
created voice print.
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Front-end processing - the "signal processing"
part, which converts the sampled speech signal
into set of feature vectors, which characterize the
properties of speech that can separate different
speakers. Front-end processing is performed both
in training- and recognition phases.
Speaker modeling - this part performs a reduction
of feature data by modeling the distributions of the
feature vectors.
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Speaker database - the speaker models are
stored here.
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Decision logic - makes the final decision about
the identity of the speaker by comparing
unknown feature vectors to all models in the
database and selecting the best matching
model.
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Speaker recognition systems fall into two
categories: text-dependent and textindependent.
If the text is same for enrollment and
verification this is called text-dependent
recognition
In a text-dependent system, prompts can either
be common across all speakers (e.g.: a
common pass phrase) or unique
In addition, the use of shared-secrets (e.g.:
passwords and PINs) or knowledge-based
information) can be employed in order to
create a multi-factor authentication scenario.
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Text-independent systems are most often used for
speaker identification as they require very little if any
cooperation by the speaker.
In this case the text during enrollment and test is
different. In fact, the enrollment may happen without
the user's knowledge, as in the case for many forensic
applications.
As text-independent technologies do not compare
what was said at enrollment and verification,
verification applications tend to also employ speech
recognition to determine what the user is saying at the
point of authentication.
Speaker Verification and Speaker
Recognition
Erorrs
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False Match Ratio(FMR)
False Non-match Rate(FNMR)
Failure To Enroll Rate
FMR
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System gives false +ve matching a user
biometrics with another user's biometrics. Type
1 error
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Occurs when two people have high degree of
similarity
It may used to eliminate the non matches. And
continue the process again.
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FNR
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User’s templates is matched with the enrolled
templates and an incorrect decision of non
match is made. Type 2 error
Due to environment, aging, sickness.
FER
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Biometric data of some user may not be clear.
Technology
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The various technologies used to process and
store voice prints include frequency
estimation, hidden Markov models, gaussian
mixture models, pattern matching algorithms,
neural networks, matrix representation and
decision trees. Some systems also use "antispeaker" techniques, such as cohort models,
and world models.
VQ Speaker Verification
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Speech Feature Extraction
Mel Frequency Cepstral Coefficients
Cepstral Coefficients
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Power of the triangular filter = summarized
Log calculated
Convert them to time domain using the
Discrete Cosine Transform (DCT)
Result is called the mel frequency cepstral
coefficients (MFCC).
Verification
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Threshold
Cohort Speakers
Ratio
Speaker Verification and Speaker
Recognition
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Accessing confidential information areas
Access to remote computers
Voice dialing
Banking by telephone
Telephone shopping
Database access services
Information services
Voice mail
PIN code for your ATM