Transcript Voice Biometrics General Description     Each individual has individual voice components called phonemes. Each phoneme has a pitch, cadence, and inflection These three give each one.

Voice Biometrics
General Description
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Each individual has individual voice components
called phonemes.
Each phoneme has a pitch, cadence, and
inflection
These three give each one of us a unique voice
sound.
The similarity in voice comes from cultural and
regional influences in the form of accents.
General Description
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According to the National Center of Voice and Speech, as one phonate, the
vocal folds and produces a complex sound spectrum made up of a range of
frequencies and overtones. As the spectrum travels through the various-sized
areas in the vocal track, some of the frequencies resonate more than others.
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Larger spaces resonate at a lower frequencies
Smaller at higher frequencies
The two largest spaces in the vocal track and, the throat, and the mouth,
produce the two lowest resonant frequencies or formants.
Certain inflections and pitches we learn from family members.
Voice physiological and behavior biometric are influenced by our body,
environment, and age.
It is possible that our voice does not always sound the same.
So is voice a good biometric?
General Description
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Formants are the resonant frequencies of the vocal
tract when vowels are pronounced. While vowels are
attributed to this periodic resonance, consonants are
not periodic. They are produced by restriction of air
flow with the mouth, tongue, and jaw.
Linguists classify each type of speech sound (called
phenomes) into different categories. In order to identify
each phenome, it is oftentimes useful to look at its
spectrogram or frequency response where one can find
the characteristic formants
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Although all phenomes have their own formants, vowel
sound formants are usually the easiest to identify
All formants have the trait of waxing and waning in
energy in all frequencies, which is caused by the
repeated closing and opening of the human vocal tract.
On average, this repeated closing and opening occurs at a rate of
125 times per second in an adult male and 250 times per second
in an adult female.
This rate gives the sensation of pitch (higher
frequencies result in higher pitches).
Formant values can vary widely from person to person,
but the spectrogram reader learns to recognize patterns
which are independent of particular frequencies and
which identify the various phonemes with a high degree
of reliability.
Vowel “A”
Vowel “I”
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Formants can be seen very clearly in a wideband
spectrogram, where they are displayed as dark bands.
The darker a formant is reproduced in the
spectrogram, the stronger it is (the more energy there
is there, or the more audible it is):
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But there is a difference between oral vowels on one
hand, and consonants and nasal vowels on the other.
Nasal consonants and nasal vowels can exhibit
additional formants, nasal formants, arising from
resonance within the nasal branch.
Consequently, nasal vowels may show one or more
additional formants due to nasal resonance, while one
or more oral formants may be weakened or missing due
to nasal antiresonance.
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Oral formants are numbered consecutively upwards
from the lowest frequency. In the example, fragment
from the previous wideband spectrogram shows the
sequence [ins] from the beginning. Five formants are
visible in this [i], labeled F1-F5. Four are visible in this
[n] (F1-F4) and there is a hint of the fifth. There are
four more formants between 5000Hz and 8000Hz in [i]
and [n] but they are too weak to show up on the
spectrogram, and mostly they are also too weak to be
heard.
The situation is reversed in this [s], where F4-F9 show
very strongly, but there is little to be seen below F4.
Individual Differences in Vowel
Production
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There are differences in individual formant
frequencies attributable to: size, age, gender,
environment, and speech.
The acoustic differences that allow us to
differentiate between various vowel productions
are usually explained by a source-filter theory.
The source is the sound spectrum created by
airflow through the glottis which varies as vocal
folds vibrate. The filter is the vocal track itselfits shape is controlled by the speaker.
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The three figures below (taken from Miller)
illustrate how different configurations of the
vocal tract selective pass certain frequencies and
not others. The first shows the configuration of
the vocal tract while articulating the phoneme [i]
as in the word "beet," the second the phoneme
[a], as in "father," and the third [u] as in "boot."
Note how each configuration uniquely affects
the acoustic spectrum--i.e., the frequencies that
are passed
Voice Capture
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Voice can be captured in two ways:
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Captured voice is influenced by two factors:
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Dedicated resource like a microphone
Existing infrastructure like a telephone
Quality of the recording device
The recording environment
In wireless communication, voice travels through open
air and then through terrestrial lines, it therefore,
suffers from great interference.
Algorithms for Voice Interpretation
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Algorithms used to capture, enroll and match
voice fall into the following categories:
Fixed phase verification
 Fixed vocabulary verification
 Flexible vocabulary verification
 Text-independent verification.
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Voice Verification
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Voice biometrics works by digitizing a profile of a
person's speech to produce a stored model voice print,
or template.
Biometric technology reduces each spoken word to
segments composed of several dominant frequencies
called formants.
Each segment has several tones that can be captured in
a digital format.
The tones collectively identify the speaker's unique
voice print.
Voice prints are stored in databases in a manner similar
to the storing of fingerprints or other biometric data.
Application of Voice Technology
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Voice technology is applicable in a variety of areas but
for us, those used in biometric technology include:
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Voice Verification
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Internet/intranet security:
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on-line banking
on-line security trading
access to corporate databases
on-line information services
PC access restriction software
Parental control
Business software as a DSP solution at check points where smart
cards or PIN used entrance / exit control points
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Voice Recognition
hands free devices, for example car mobile hands free sets
 electronic devices, for example telephone, PC, or ATM cash
dispenser
 software applications, for example games, educational or office
software
 industrial areas, warehouses, etc.
 spoken multiple choice in interactive voice response systems,
for example in telephony
 applications for people with disabilities
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Voice verification systems are different from voice recognition
systems although the two are often confused.
Voice recognition is used to translate the spoken word into a
specific response. The goal of voice recognition systems is
simply to understand the spoken word, not to establish the
identity of the speaker. A good familiar example of voice
recognition systems is that of an automated call center asking a
user to “press the number one on his phone keypad or say the
word ‘one’.” In this case, the system is not verifying the identity
of the person who says the word “one”; it is merely checking
that the word “one” was said instead of another option.
Voice verification verifies the vocal characteristics against those
associated with the enrolled user.
The US PORTPASS Program, deployed at remote locations
along the U.S.–Canadian border, recognizes voices of enrolled
local residents speaking into a handset. This system enables
enrollees to cross the border when the port is unstaffed.
How is voice recognition performed?
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Voice recognition can be divided into two classes:
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template matching - template matching is the simplest technique and has
the highest accuracy when used properly, but it also suffers from the most
limitations.
feature analysis
The first step is for the user to speak a word or phrase into a
microphone.
The electrical signal from the microphone is digitized by an
"analog-to-digital (A/D) converter", and is stored in memory.
To determine the "meaning" of this voice input, the computer
attempts to match the input with a digitized voice sample, or
template, that has a known meaning.
This technique is a close analogy to the traditional command
inputs from a keyboard. The program contains the input
template, and attempts to match this template with the actual
input using a simple conditional statement.
The two stages of a biometric system
Software
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Open Source Speech Software from Carnegie Mellon
University
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Hephaestus: Open Source activities at Carnegie Mellon
CMU Sphinx recognition engines -- Sphinx 2, Sphinx 3, Sphinx 4, and
SphinxTrain.
PocketSphinx Sphinx for embedded platforms.
Festvox Project speech synthesis engines, voices and tools
CMU Statistical Language Modeling Toolkit (CMU SLM)
CMUdict -- pronunciation dictionary
OpenVXI -- VoiceXML browser
SALT browser - finally online!
Audio Databases -- AN4, Microphone array, etc
RavenClaw-Olympus Dialog system development toolkit.
We will try CMU Sphinx Group Open Source Speech
Recognition
http://cmusphinx.sourceforge.net/html/cmusphinx.php