Introduction to Biometrics

1
Introduction to Biometrics

• Dr. Bhavani Thuraisingham
• The University of Texas at Dallas
• Lecture 12
• Biometric Technologies Voice Scan
• October 3, 2005

2
Outline

• Introduction
• How does it work
• Components
• Voice Scan Process
• Template generation and matching
• Market and Applications
• Strengths and Weaknesses
• Research Directions
• Summary
• Appendix Banking Application

3
References

• Course Text Book, Chapter 7
• http//www.biometricsinfo.org/voicerecognition.htm
  

4
Introduction

• Voice Recognition is a technology which allows a
  user to use his/her voice as input.
• Voice recognition may be used to dictate text
  into the computer or to give commands to the
  computer (such as opening application programs,
  pulling down menus, or saving work).
• Older voice recognition applications require each
  word to be separated by a distinct space.
• This allows the machine to determine where one
  word begins and the next stops.
• These kinds of voice recognition applications are
  still used to navigate the computer's system, and
  operate applications such as web browsers or
  spread sheets.

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Introduction (Continued)

• Newer voice recognition applications allow a user
  to dictate text fluently into the computer.
• These new applications can recognize speech at up
  to 160 words per minute.
• Voice recognition uses a neural net to "learn" to
  recognize voice.
• As you speak, the voice recognition software
  remembers the way you say each word.
• This customization allows voice recognition, even
  though everyone speaks with varying accents and
  inflection.
• In addition to learning how you pronounce words,
  voice recognition also uses grammatical context
  and frequency of use to predict the word you wish
  to input.
• While the accuracy of voice recognition has
  improved users still experience problems with
  accuracy either because of the way they speak or
  the nature of their voice.
•  

6
How does it work?

• Voice recognition technology utilizes the
  distinctive aspects of the voice to verify the
  identity of individuals.
• Voice recognition is occasionally confused with
  speech recognition, a technology which translates
  what a user is saying (a process unrelated to
  authentication).
• Voice recognition technology, by contrast,
  verifies the identity of the individual who is
  speaking.
• The two technologies are often bundled speech
  recognition is used to translate the spoken word
  into an account number, and voice recognition
  verifies the vocal characteristics against those
  associated with this account. 

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How does it work? (Concluded)

• Voice recognition can utilize any audio capture
  device, including mobile and land telephones and
  PC microphones.
• The performance of voice recognition systems can
  vary according to the quality of the audio signal
  as well as variation between enrollment and
  verification devices
• During enrollment an individual is prompted to
  select a passphrase or to repeat a sequence of
  numbers.
• The passphrases selected should be approximately
  1-1.5 seconds in length very short passphrases
  lack enough identifying data, and long passwords
  have too much, both resulting in reduced
  accuracy.
• The individual is generally prompted to repeat
  the passphrase or number set a handful of times,
  making the enrollment process somewhat longer
  than most other biometrics. 

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Components of the Voice Scan System

• Users spoken phrase is converted from analog to
  digital formant and transmitted to local or
  central PC
• For desktop verification applications, engine
  that provides templates based functions may
  reside on the local or central PC
• For telephone based applications the software may
  reside in the Institution that users are
  interacting with
• Voice scan comparisons are tied directly to
  existing authenticating systems
• May be web-enabled

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Process

• Data Acquisition
• Audio capture devices include mobile and land
  telephones and PC microphones
• Individual selects a passphrase and repeats it or
  repeats sequence of numbers
• Should be long enough
• Not too loud or soft
• More difficult with PC/ mobile phones than with
  land telephones
• Data Processing
• The data is proceed before template creation
• Eliminates gaps and performs filtering

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Distinctive Features

• Measures vocal qualities not detectable by humans
• Pitch and frequency are key features measured
• Voice scan algorithms also measure
• gain or intensity
• short time spectrum of speech,
• format frequencies,
• linear prediction coefficients,
• cepstral coefficient
• Spectrograms
• Nasal coarticulation
• Replicable only by human voice and therefore more
  secure

11
Template Creation/Generation

• Based on statistics based pattern matching called
  Hidden Markov Models (HMM)
• HMM are generalized profiles that are formed
  through the comparison of multiple samples to
  find characteristically repeating patterns
• During enrollment template generation relies on
  the capture of multiple voice samples and are
  analyzed to determine the qualities that can be
  relied upon for later recognition

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Template Matching

• Production voice scan technologies are not
  capable of one-many identification
• Operates in one-one authentication mode
• When user attempts verification the system
  compares the live submission with the profile
  created and then returns a statistical rating
• Users may change their speech during enrollment
  and verification and therefore not very reliable

13
Applications

• Voice recognition is a strong solution for
  implementations in which vocal interaction is
  already present.
• It is not a strong solution when speech is
  introduced as a new process.
• Telephony is the primary growth area for voice
  recognition, and will likely be by far the most
  common area of implementation for the technology.
  
• Telephony-based applications for voice
  recognition include account access for financial
  services, customer authentication for service
  calls
• These solutions route callers through enrollment
  and verification subroutines, using
  vendor-specific hardware and software integrated
  with an institution's existing infrastructure. 
• Voice recognition has also been implemented in
  physical access solutions for border crossing

14
Deployment

• NYC Department of Corrections NY DOC
• Used to check the location of Juvenile offenders
• The offender is called and he/she has to call
  back. The voice is verified and also caller ID is
  checked
• Pilot projects in banking
• Ireland, Belgium, South Africa
• Technology form T-NETIX and Buytel

15
Market

• Though revenues from the technology are
  relatively small today, voice recognition will
  likely draw substantially greater revenues
  through 2007.
• Most likely to be deployed in telephony-based
  environments (such as account access for
  financial services and customer authentication
  for service calls).
• Voice recognition revenues are projected to grow
  from 12.2m in 2002 to 142.1m in 2007.
• Voice recognition revenues are expected to
  comprise approximately 4 of the entire biometric
  market.

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Strengths of Voice Scan

• One of the challenges facing large-scale
  implementations of biometrics is the need to
  deploy new hardware to employees, customers and
  users.
• One strength of telephony-based voice recognition
  implementations is that they are able to
  circumvent this problem, especially when they are
  implemented in call center and account access
  applications.
• The ability to use existing telephones means that
  voice recognition vendors have hundreds of
  millions of authentication devices available for
  transactional usage today. 
• Resistant to Imposters
• Imposter may not guess correct passphrases and
  account numbers

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Weaknesses of Voice Scan

• There may be noise with the voice
• Low accuracy as enrollment voice may differ from
  verification voice for the same user
• Large template size
• Does not work well with PC

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Research Directions

• Improve accuracy
• Model variations of voice for the same speaker
• Improve performance
• Better PC-based methods
• Better models
• HMM, neural networks

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Technology Comparison

• Method Coded Pattern Misidentification
  rate Security
• Iris Recognition Iris pattern 1/1,200,000
• Fingerprinting Fingerprints 1/1,000
• Hand Shape Size, length and thickness of
  hands 1/700
• Facial Recognition Outline, shape and
  distribution of eyes and nose 1/100
• Signature Shape of letters, writing order, pen
  pressure 1/100
• Voiceprinting Voice characteristics 1/30

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Summary

• Can be widely used
• Telephones available
• Low accuracy
• People can change voices
• Many applications
• E.g., Banking Telephony

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Introduction to Biometrics

• Dr. Bhavani Thuraisingham
• The University of Texas at Dallas
• Telephone Banking Application of Voice Scan
• October 3, 2005

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The Problem

• Telephone banking is increasingly popular with
  customers, and will be increasingly attractive to
  banks and other financial institutions as they
  start to implement highly cost effective
  automated speech recognition technology to handle
  routine transactions (the subject of another
  "financial futures" web page).
• But the procedures for verifying customers over
  the telephone are unsatisfactory, both in terms
  of customer convenience and also, increasingly,
  from a security point of view.
• The Problem The usual approach to verifying
  customers - proving that they are who they claim
  to be - is to use some sort of PIN or password.
  To avoid the customer having to say the password
  out loud, they are usually prompted for, say, the
  second and fourth letters in the password.

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The Problem (Concluded)

• There are several problems with this approach
• Firstly, passwords and PINs are difficult to
  remember and unwieldy for customers to use in
  this manner.
• Secondly, it takes time - identification and
  verification of the caller is often the
  lengthiest component of a transaction and this
  translates directly to the bottom line.
• Many customers write down their passwords or
  reveal them to the operator (in extreme cases
  they may self select the same PIN that they use
  for ATM withdrawals).
• Many call centers prompt the caller for
  additional 'secret' items such as their mother's
  maiden name, but this only exacerbates the other
  two problems.

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The Solution

• The Solution? Voice Verification Technology now
  exists which enables individuals to be reliably,
  rapidly and cost-effectively verified on the
  basis of the physical characteristics of their
  voice.
• Vendors now supply commercial voice verification
  technology.
• A good example is Nuance Communications, based in
  California, using essentially the same technology
  which underlies their speaker independent speech
  recognition software.
• But in this case recognition is speaker dependent
  - the customer is only allowed to use the system
  if their individual voiceprint matches their
  identity (normally established though an account
  number).

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The Solution (Concluded)

• A new customer automatically enrolls in the
  system over the telephone by repeating about 10
  four digit numbers or reading a short piece of
  text.
• The software extracts from this a number of
  physical characteristics which are unique to that
  voice.
• In all subsequent transactions, the caller, once
  identified, is asked to repeat a couple of
  randomly generated PINs or, for example, names of
  cities (this is to prevent imposters
  tape-recording a customer saying their password
  or PIN).
• If the voiceprint matches the one stored against
  the account number the transaction proceeds if
  not, the customer is referred to a supervisor.

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Details

• The speaker-specific characteristics of speech
  are due to differences in physiological and
  behavioral aspects of the speech production
  system in humans.
• The main physiological aspect of the human speech
  production system is the vocal tract shape.
• The vocal tract is generally considered as the
  speech production organ above the vocal folds,
  which consists of the following
• (i) laryngeal pharynx
• (ii) oral pharynx
• (iii) oral cavity
• (iv) nasal pharynx
• (v) nasal cavity

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Details (Continued)

• The vocal tract modifies the spectral content of
  an acoustic wave as it passes through it, thereby
  producing speech.
• Hence, it is common in speaker verification
  systems to make use of features derived only from
  the vocal tract.
• In order to characterize the features of the
  vocal tract, the human speech production
  mechanism is represented as a discrete-time
  system
• The acoustic wave is produced when the airflow
  from the lungs is carried by the trachea through
  the vocal folds.
• This source of excitation can be characterized as
  phonation, whispering, frication, compression,
  vibration, or a combination of these.

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Details (Continued)

• Phonated excitation occurs when the airflow is
  modulated by the vocal folds.
• Whispered excitation is produced by airflow
  rushing through a small triangular opening
  between the arytenoid cartilage at the rear of
  the nearly closed vocal folds.
• Frication excitation is produced by constrictions
  in the vocal tract.
• Compression excitation results from releasing a
  completely closed and pressurized vocal tract.
• Vibration excitation is caused by air being
  forced through a closure other than the vocal
  folds, especially at the tongue.

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Details (Continued)

• Speech produced by phonated excitation is called
  voiced,
• Produced by phonated excitation plus frication is
  called mixed voiced
• Produced by other types of excitation is called
  unvoiced.
• It is possible to represent the vocal-tract in a
  parametric form as the transfer function H(z).
• In order to estimate the parameters of H(z) from
  the observed speech waveform, it is necessary to
  assume some form for H(z).
• Ideally, the transfer function should contain
  poles as well as zeros.
• However, if only the voiced regions of speech are
  used then an all-pole model for H(z) is
  sufficient.

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Details (Concluded)
31
Choice of Features

• The LPC (linear predictive coding) features were
  very popular in the early speech-recognition and
  speaker-verification systems.
• However, comparison of two LPC feature vectors
  requires the use of computationally expensive
  similarity measures
• Hence LPC features are unsuitable for use in
  real-time systems.
• The use of the cepstrum has been suggested,
  defined as the Inverse Fourier transform of the
  logarithm of the magnitude spectrum, in
  speech-recognition applications.
• The use of the cepstrum allows for the similarity
  between two cepstral feature vectors to be
  computed as a simple Euclidean distance.
• It has been demonstrated that the cepstrum
  derived from the LPC features results in the best
  performance
• Consequently, LPC derived cepstrum for speaker
  verification system is used in general.

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Spoeaker Modeling

• Using cepstral analysis, an utterance may be
  represented as a sequence of feature vectors.
• Utterances spoken by the same person but at
  different times result in similar yet a different
  sequence of feature vectors.
• The purpose of voice modeling is to build a model
  that captures these variations in the extracted
  set of features.
• There are two types of models that have been used
  extensively in speaker verification and speech
  recognition systems
• stochastic models and template models.

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Speaker Modeling (Continued)

• The stochastic model treats the speech production
  process as a parametric random process and
  assumes that the parameters of the underlying
  stochastic process can be estimated in a precise,
  well defined manner.
• The template model attempts to model the speech
  production process in a non-parametric manner by
  retaining a number of sequences of feature
  vectors derived from multiple utterances of the
  same word by the same person.
• Template models dominated early work in speaker
  verification and speech recognition because the
  template model is intuitively more reasonable.
• However, recent work in stochastic models has
  demonstrated that these models are more flexible
  and hence allow for better modeling of the speech
  production process.

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Speaker Modeling (Concluded)

• A very popular stochastic model for modeling the
  speech production process is the Hidden Markov
  Model (HMM).
• HMMs are extensions to the conventional Markov
  models, wherein the observations are a
  probabilistic function of the state
• the model is a doubly embedded stochastic process
  where the underlying stochastic process is not
  directly observable (it is hidden).
• The HMM can only be viewed through another set of
  stochastic processes that produce the sequence of
  observations.
• Thus, the HMM is a finite-state machine, where a
  probability density function p(x s_i) is
  associated with each state s_i. The states are
  connected by a transition network, where the
  state transition probabilities are a_ij p(s_i
  s_j).

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Pattern Matching

• The pattern matching process involves the
  comparison of a given set of input feature
  vectors against the speaker model for the claimed
  identity and computing a matching score. For the
  Hidden Markov models the matching score is the
  probability that a given set of feature vectors
  was generated by the model.
•