Voice Biometrics

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17 Νοε 2013 (πριν από 3 χρόνια και 6 μήνες)

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Voice Biometrics

General Description

Each individual has individual voice
components called

Each phoneme has a
, and

These three give each one of us a unique voice

The similarity in voice comes from cultural and
regional influences in the form of accents.

General Description

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
areas in the vocal track, some of the frequencies resonate more than others.

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?


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

General Description

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 “I”

Vowel “A”

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):

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.

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

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

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
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 itself

its shape is controlled by the speaker.

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

Voice can be captured in two ways:

Dedicated resource like a microphone

Existing infrastructure like a telephone

Captured voice is influenced by two factors:

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

Algorithms used to capture, enroll and match
voice fall into the following categories:

Fixed phase verification

Fixed vocabulary verification

Flexible vocabulary verification

independent verification.

Voice Verification

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

Voice technology is applicable in a variety of areas but
for us, those used in biometric technology include:

Voice Verification

Internet/intranet security:

line banking

line security trading

access to corporate databases

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

Voice Recognition

hands free devices, for example car mobile hands free sets

electronic devices, for example telephone, PC, or ATM cash

software applications, for example games, educational or office

industrial areas, warehouses, etc.

spoken multiple choice in interactive voice response systems,
for example in telephony

applications for people with disabilities

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?

Voice recognition can be divided into two classes:

template matching

template matching is the simplest technique and has
the highest accuracy when used properly, but it also suffers from the most

feature analysis

The first step is for the user to speak a word or phrase into a

The electrical signal from the microphone is digitized by an
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


Open Source Speech Software from Carnegie Mellon

: Open Source activities at Carnegie Mellon

CMU Sphinx

recognition engines

Sphinx 2, Sphinx 3, Sphinx 4, and


Sphinx for embedded platforms.

Festvox Project

speech synthesis engines, voices and tools

CMU Statistical Language Modeling Toolkit




pronunciation dictionary





SALT browser


finally online!

Audio Databases


AN4, Microphone array, etc


Dialog system development toolkit.

We will try CMU Sphinx Group Open Source Speech