Voice Biometrics p66 Markowitz

“It’s me!”

This pronouncement is usually made over the telephone or at an entryway out

of sight of the intended hearer. It embodies the expectation that the sound of

one’s voice is sufficient for the hearer to recognize the speaker. In short, “It’s me!” is

the original real-world, speaker-recognition challenge.

Voice Biometrics

66 September 2000/Vol. 43, No. 9 COMMUNICATIONS OF THE ACM

Who are you? Your voice alone can be used to verify your personal identity—unobtrusively and invisibly.

�Judith A. Markowitz

It is possible today to automate a growing numberof speaker-recognition tasks with such technologies asspeaker verification and speaker identification. Likehuman listeners, voice biometrics1 use the features of aperson’s voice to ascertain the speaker’s identity. Sys-tems performing this function have been applied toreal-world security applications for more than adecade. Their use is increasing rapidly in a broad spec-trum of industries, including financial services, retail,corrections, even entertainment. Here, I provide anoverview of speaker verification and speaker identifica-tion, focusing on deployed, real-world technologiesand the types of applications being used today.

Voice-biometrics systems can be categorized asbelonging in two industries: speech processing andbiometric security (see Figure 1). This dual parentage

has strongly influenced how voice-biometrics toolsoperate in the real world.

Speech processing. Like other speech-processingtools, voice biometrics extract information from thestream of speech to accomplish their work. They canbe configured to operate on many of the same acousticparameters as their closest speech-processing relative—speech recognition. And like speech recognition, theybenefit from lots of data, good microphones, and noisecancellation software. Voice biometrics are vulnerableto some of the same conditions that cause speech-recognition systems to perform poorly: backgroundand channel noise; variable and inferior microphonesand telephones; and extreme hoarseness, fatigue, orvocal stress.

There are also important differences between voice-biometrics systems and other speech-processing tech-nologies, including speech recognition. The mostsignificant is that voice biometrics technologies do notknow what a person is saying, relying on speech recog-nition to do that. Moreover, the trend toward speakerindependence that characterizes speech recognitioncannot exist for voice biometrics. By definition, voicebiometrics are always linked to a particular speaker. As

1Speech-processing researchers prefer the term “speaker recognition.” People outside

the speech-processing industry often confuse it with “speech recognition,” which

refers to a speech-processing technology that recognizes what a person is saying. This

confusion has led to some use of speech-recognition tools for security applications

beyond the abilities of speech-recognition technology. The result is a weak form of

password or passcode security. “Voice recognition,” another confusing term, is often

used to refer to speech recognition but misleadingly suggests some speaker identifica-

tion is involved.

COMMUNICATIONS OF THE ACM September 2000/Vol. 43, No. 9 67

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a result, they require some type of enrollment foreach user. The need for enrollment is an attributevoice biometrics shares with its relatives in the bio-metric-security industry.

Biometric security. Membership in the biometricsindustry influences how voice-biometrics systems areused. Biometrics-based technologies are applied mostoften in security, monitoring, and fraud preventionwhere they positively identify individuals and distin-guish one person from another. These abilities differ-entiate biometrics from all other forms of automatedsecurity. A card system can, at best, determine onlywhether a person has a viable access card, and pass-word security can determine only whether the personknows the proper password. None of them verifythat the person presenting the card or entering thepassword is the individual authorized to do so.

Biometric systems determine whethera biometric sample, such as a fin-gerprint or spoken password,comes from a specific indi-vidual by comparing thatsample with a referencebiometric—a sample ofthe same type of biometricprovided by the individualin question. Developers ofvoice biometrics called thisa “reference voiceprint.” Aswith reference templatesfor other biometrics, refer-ence voiceprints are evaluatedin terms of the number of timesthey mistakenly accept a falseclaim of identity as a legitimateclaim and the number of times theyreject a legitimate speaker as an impostor.

The most significant difference between voice bio-metrics and other biometrics is that voice biometricsare the only commercial biometrics that processacoustic information. Most other biometrics areimage-based. Another important difference is thatmost commercial voice biometrics systems aredesigned for use with virtually any standard tele-phone on public telephone networks. The ability towork with standard telephone equipment makes itpossible to support broad-based deployments ofvoice biometrics applications in a variety of settings.In contrast, most other biometrics require propri-etary hardware, such as the vendor’s fingerprint sensor or iris-scanning equipment. This distinc-

tion—standard versus proprietary input device—isbeginning to disappear. The recent development ofinexpensive, high-quality cameras, for example, nowenables wider deployment of face-recognition applications.

Types of Voice BiometricsThe following sections outline the best-known com-mercialized forms of voice biometrics: speaker verifi-cation and speaker identification.

Speaker verification. Speaker-verification systemsauthenticate that a person is who she or he claims tobe. If, for example, the person speaking at the begin-ning of this article had shouted, “It’s Julie,” ratherthan, “It’s me!,” the intended hearer would have toperform speaker verification based on that identityclaim.

Figure 2 shows a typical speaker-verificationprocess. It begins with a claim of identity, such asentering an account number or ID code, presentinga credit card or ATM card, or allowing the system toaccess the ID of a user-linked input device, such as aspecific cellular telephone. Typically, a person entersthe ID manually using a telephone keypad or key-board. If the system uses speech recognition in con-junction with speaker verification, it may requestverbal input of an ID code.

The system uses the ID to retrieve the referencevoiceprint for the person authorized to use the ID, asin Figure 2. It then requests a sample of speech from


the claimant. The newly input speech is converted intoa voiceprint and compared to the reference voiceprint.The results of the comparison are quantified and com-pared to an acceptance/rejection threshold to determinewhether the two voiceprints are similar enough for thesystem to accept the identity claim.

Figure 3 shows several ways of interacting withspeaker-verification systems. Most commercial systemsare text-dependent. They request a password, account

number, or some other prearrangedcode. Because it requests a pass-word, the system in Figure 3a istext-dependent. Text-dependentsystems provide what the data-secu-rity industry calls “strong authenti-cation.”2 Strong authenticationrequires the use of at least two dif-ferent kinds of security. In the caseof text-dependent speaker verifica-tion, the person must have the cor-rect voice (an example of “Who youare” security) and also know theproper password (an example of“What you know” security).

The system in Figure 3b displays a text-dependent,voice-only approach that uses the account number asboth identity claim and password. Speech recognitiondecodes the input, and speaker verification uses thesame input as the biometric sample it compares to thereference voiceprint.

Figure 3c shows an example of “text-prompted”technology.3 Text-prompted systems ask the speaker torepeat a series of randomly selected digit strings, wordsequences, or phrases. Text prompting requires longerenrollment than text-dependent technology, becausethe reference voiceprint it generates must contain allthe components that will be used to construct chal-lenge-response variants. As Figure 3c indicates, verifica-tion also takes longer.

Text prompted verification is well-suited to high-security and high-risk systems, such as those used tomonitor felons in home-incarceration and commu-nity-release programs. Text prompting is also usefulwhen there is legitimate concern about the use ofsophisticated recordings of impostors, because theresponses requested from the user are selected ran-domly, making it difficult to create and play a taperecording with the requested items in the propersequence.

Relatively few applications require text prompting,because it’s fairly difficult to defeat a good commercialspeaker-verification system with a recording. The voicesignal input into a microphone or telephone held closeto the speaker’s mouth differs markedly from a signalcaptured even as close as a foot away from the speaker.Moreover, many commercial speaker-verification sys-tems look for telltale auditory signals, distortions, exactmatches, and other indications that a recording hasbeen used. As a result, creating a recording that can fool


Speech Processing Biometrics

Output

Speech SynthesisDigitized Speech

SpeakerVerification

VoiceBiometrics

Input

Signature Verification

Typing Dynamics

Face RecognitionFinger Geometry

FingerprintingHand GeometryIris/Retina ScanSpeech Recognition

SpeakerIdentification

DNA

Figure 1. The voice-biometrics family tree.

Figure 2. Speaker verification.

Voiceprintdatabase

Merry -go-

round

Rejectionprocedure

Rejectionprocedure

Get claim of identity

Get referencevoiceprint

Get sample of speech

Convert tovoiceprint

Comparevoiceprints

Compare score withthreshold

No

No

Yes

Yes

Acceptclaim?

Valid ID?

Matchingscore

Identityclaim

Merry go

round

Transfer toapplication

2The three basic classes of security are: what you have (such as a key, a card, or a

token); what you know (such as a password or a PIN); and who you are (biometrics).3Vendors have begun using the term “challenge-response” to refer to these systems.

these systems is a difficult and costly challenge. Text-independent verification accepts any spoken

input, making it possible to design unobtrusive, eveninvisible, verification applications that examine theongoing speech of an individual. The ability of text-independent technology to operate unobtrusively andin the background makes it attractive for customer-related applications, because customers need not pausefor a security check before moving on to their primarybusiness objective. For example, the text-independentspeaker verification in Figure 3d is invoked by an agentin a bank’s customer-service call center. The system ver-ifies that the caller—who has requested a large transac-tion—is indeed the authorized customer. Anotherapplication of this technology, now under develop-ment, is continuous speaker verification over wirelessmobile telephones [1].

Text-independent technology is much more difficultto implement than text-dependent or text-promptedtechnology. It requires longer samples of speech and ismore sensitive to the acoustic quality of the input.Another potential concern centers on privacy. The

application’s ability to operate in the background givestext-independent technology the potential for beingused without the subject’s knowledge.

Speaker identification. Speaker identification4

assigns an identity to the voice of an unknown speaker.The assertion “Its me!” requires speaker identification,because the intended hearer is expected to assign theproper identity to the speaker based on the voice alone.In most cases, speaker identification is more difficultthan speaker verification, because it involves multiplecomparisons of utterances that are likely to be differentfrom each other and may not have been recorded withcomparable equipment. In forensic and intelligenceapplications, for example, the stored samples may havebeen acquired by hidden equipment at a distance fromthe speaker in a noisy environment. When a suspect isincarcerated, forensic speaker identification may be eas-ier to perform if the court permits multiple recordingsto be made of the suspect’s voice using equipment com-parable to that used when the crime was committed.

Unlike speaker verification, speaker identificationdoes not expect to receive a claim of identity. Process-ing begins when a sample of speech from of anunknown speaker is presented to the system (see Figure4); the sample may be live or recorded. It is almostalways text-independent and in some applications,such as those in law-enforcement, the speaker may notknow the sample was even taken. The system thenconverts the sample into a voiceprint and systematicallycompares the new voiceprint with all or with a specifiedsubset of the system’s reference voiceprints.


Figure 3. Types of speaker verification.

(a) Text-dependent verification.

(b) Text-dependent verification with speech recognition.

(c) Text-prompted verification.

(d) Text-independent verification.

Step 1: System: Please enter your account number using your touch-tone keypad

Caller: 235167Step 2: System: Please say your password Caller: Merry go round

System: Thank you.

Step 1: System: Please enter your account number using your touch-tone keypad

Caller: 235167Step 2: System: Please say 42-69 Caller: 42-69

System: Please say 83-24 Caller: 83-24

System: Please say 99-48 Caller: 99-48

System: Thank you.

Call Center Agent with caller ID: What can I do for you, Ms. Jones?

Caller: I want to transfer $100,000 from my savings account with you to an offshore account in

Bimini that I have just opened.Agent: Let me look into that. Please wait while I am getting the information

PAUSE while the system is verifying the identity of the caller

Agent: Thank you for your patience. I will process that request now.

System: Please say your account number Caller: 235167System: Thank you.

Voiceprintdatabase

If you don't pay us one million dollars by Friday we will blow up your factory. If you

don't ...

Get sample of speech

Convert tovoiceprint

Get referencevoiceprint

Comparevoiceprints

Rank reference

voiceprints

No

Yes

Lastvoiceprint?

Matchingscore

Figure 4. Speaker identification.

4Speaker identification is sometimes called “speaker recognition.” This lack of preci-

sion is unfortunate, because the term also refers to the entire class of “voice-biomet-

rics.” The resulting ambiguity is another reason I prefer the term “voice biometrics”

for referring to the class of speaker-identity technologies.

The system in Figure 4 was configured to rank thereference voiceprints in terms of how likely they are tocontain the voice of the person generating the sample.Other systems select one or two potential identitiesfrom among the reference voiceprints. Some imple-mentations allow the system to report that the voicedoes not match any of the reference voiceprints in itsdatabase. This strategy has been applied to controllingcellular toll fraud when more than one person may beauthorized to place calls from a specified phone. If thevoice sample supplied by the caller fails to match the setof reference voiceprints associated with a mobile phone,the caller is not allowed to place calls with that phone.

Enhancing performance. As in speech recognition,the performance of voice-biometrics systems isadversely affected by noise in the telephone channeland by other acoustic variability. Developers of speech-recognition tools can build models of network noiseinto complex, data-rich speaker-independent wordmodels. By contrast, voice biometrics work withspeaker-dependent models created from a limitedamount of data spoken on a particular telephone.

Cross-channel and cross-device mismatches arisewhen a person enrolls on one device or network (suchas a high-quality wireline telephone in an office) andattempts to voice-verify on a device or network with

markedly different acoustic proper-ties, such as an inexpensive wirelesstelephone.

The most common approachesto attenuating noise, as well aschannel and device mismatches,are voiceprint adaptation, cohortmodeling, and world models.Voiceprint adaptation involvesmodification of the originalenrolled voiceprint to incorporatedata from successful verificationsinto the statistics of the originalmodel. Adaptation makes it possi-ble to include acoustic informationabout the range of devices a personuses for verification into the voice-print. Adaptation can also updatethe voiceprint with regard to varia-tions in the person’s voice. Forexample, a person may speak quitedifferently when under stress orwhen tired.

Cohort modeling and worldmodels are performance-enhance-ment techniques used in speakerverification. Systems that usecohort modeling identify individu-

als whose voices are similar to the voice of a newlyenrolled user. During verification, the system comparesthe new input to each of the cohorts, as well as to thevoiceprint of the person whose identity is beingclaimed. A world model is a group model containing aspectrum of voices. Systems using world models per-form only two comparisons: one with the voiceprintfor the claimed identity and one with the world model.The basic assumption underlying the use of cohortsand world models is that new input from authorizedusers will match their reference voiceprints better thanthey match the voiceprints of other people—even inadverse conditions.

Commercial Applications and TrendsMost commercial applications of voice biometrics pro-vide security, fraud prevention, or monitoring; seeTable 1 for a partial list of deployed applications inthese three areas. The applications described in the fol-lowing sections reflect the dominant trends in com-mercial deployment; the majority arespeaker-verification applications, most of which usetext-dependent technology. They also reflect the diver-sity and creativity being applied to real-world imple-mentations of voice biometrics.

Data security (Illinois Department of Revenue). The


Data and data networks

Physical/site access

Telephone network security(toll fraud)

Transaction security

Time and attendance monitoring

Corrections monitoring

Security

Fraud Prevention

Monitoring

Function Application Type

BMC Software. Password reset (over the telephone) using virtual help desk.Illinois Department of Revenue. Off-site access to secure data networks.INTRUST Bank. Internal wire transfers

U.S. Immigration and Naturalization Service. Entry to U.S. and Canada during off hours; port of entry at Scobey, Mont.Girl Tech. Door access control system and locked box for children.City of Baltimore. Evening and weekend access to the five main city buildings.

University of Maryland, College Park. Toll-free long-distance lines for faculty and staff.GTE TSI. Integration of speaker verification into wireless security package offered to carriers.Home Shopping Network. Automated product-ordering over the telephone.Glenview State Bank. Transfer of money between accounts of a bank customer.

SOC Credit Union. Time and attendance of part-timeemployees.Salvation Army. Time and attendance of Salvation Armyworkers.New York City Department of Probation. Tracking of juvenile and adult probationers.Dane County Jail, Madison, Wisc. Monitoring ofhome-incarcerated offenders.

Example

Table 1. Example deployed applications.

Illinois Department of Revenue (IDOR) is the taxingbody for the state of Illinois, collecting tax and otherfinancial information from individuals and businessesand storing it in databases protected by several levels ofsecurity. One of IDOR’s responsibilities is to performtax audits of Illinois businesses and out-of-state compa-nies doing business in Illinois. Audits are done on thepremises of the audited business, which can be as faraway as California and Florida. In the course of per-forming an audit, an auditor may need informationfrom the IDOR databases. Before speaker verificationwas installed, the auditor called a supervisor who hadan authorized person transfer the data to a disk and

send it to the auditor. By the time the auditor receivedit, the information could be as much as 10 days old.

In 1994, IDOR installed a text-dependent speaker-verification system, configuring it to require anyonetrying to access its computer network to voice-verifyover a telephone line before being connected to a dataline [3, 7]. The system has been in continuous opera-tion since that time and expanded to 664 users, includ-ing IDOR managers, programmers, and selectedindividuals from other government agencies, such asthe Illinois comptroller’s office, the Illinois secretary ofstate, and the U.S. Internal Revenue Service. It allowsmanagers to take laptops to meetings while maintain-ing email contact with people at the agency. It alsoenables programmers on call to work from home dur-ing emergencies after work hours or on weekends. Priorto the installation of speaker-verification, some pro-grammers would have to drive up to 30 miles to IDORfacilities to handle emergencies.

IDOR officials report they have seen no evidencethat any unauthorized person has gotten into its data-bases since speaker verification was installed.

Physical access (Girl Tech, Inc.). Girl Tech developsand sells products and services reflecting the play pref-erences of pre-teen girls [6] and is committed to mak-ing technology more accessible to them. The mainconstraints on integration of advanced technology intoGirl Tech products are cost, size, and ease-of-use.

Privacy is an important issue for pre-teen and early-teen girls concerned about siblings entering their roomsto read their diaries or borrow their things. Addressingthe need for privacy, Girl Tech incorporated chip-basedtext-dependent speaker verification into its Door Passand Password Journal products. Door Pass is a brightlycolored plastic device that attaches to a bedroom door.A child activates its motion sensor by pressing theon/off button; whenever the door moves, Door Passdemands the password. If the proper password is notsupplied in the correct voice, Door Pass registers anintruder and sounds an alarm. When the child returns,Door Pass welcomes her and reports the number of

intruders it foiled during her absence.Password Journal is a password-protected plastic box

that stores a diary or other personal items. Anyone seek-ing to open Password Journal must say the correct pass-word in the proper voice. Like Door Pass, PasswordJournal reports the number of intruders attempting toopen it.

Transaction security (Home Shopping Network).Home Shopping Network (HSN), a division of USANetworks, Inc., pioneered the electronic retailingindustry in 1977. Its 24-hour-a-day programmingreaches 74 million households through broadcasts,cable, and satellite dishes. It has 3.8 million active cus-tomers (called “members”) and enrolls approximately7,000 new members a day [3, 5]. Members buy itemsby calling one of HSN’s toll-free telephone numbers.Orders can be placed with an agent or through an inter-active voice-response system using touch-tone input.The company’s call volume and 24-hour-a-day pro-gramming make automated transactions a financial andcustomer-service necessity. However, HSN has twomain reservations about the touch-tone system. Almost30% of its members cannot use it, because they do nothave touch-tone telephones. It is also vulnerable tofraud perpetrated by anyone who knows a member’sidentification codes.

Starting in 1999, HSN began deploying a hands-free, member-authentication system on its 800-num-


Future applications will be text-independentand combine voice biometrics with other speech and biometric technologies.

bers. Now, when a member calls, the system answerswith “Welcome to Home Shopping Network. Pleasespeak your telephone number, starting with the areacode.” Speech recognition decodes the spoken input. Ifthe telephone number is not registered already, themember is transferred to the enrollment procedure. Ifmore than one person is enrolled for that number, thesystem uses speaker identification to determine which,if any, of the members associated with the number iscalling. Otherwise, it uses speaker verification. Callerswho are voice-verified successfully are transferred to theinteractive voice-response product-ordering module.When verification fails, the caller is transferred to anoperator.

By the end of June 2000, HSN had installed the sys-tem on 9 of its 10 800-numbers and expected to com-plete work on the main 800-number soon after. Morethan 450,000 members were enrolled, and verification

was reported to be performing at a 98% rate. Corrections monitoring (New York City Depart-

ment of Probation). The New York City Departmentof Probation is the second largest probationary agencyin the U.S., annually supervising 90,000 adults (about60,000 at any one time) and 4,000 juveniles. ShortTerm Alternative to Remand Treatment (START) is aprogram the Department established in 1993 as analternative sentencing option for defendants with splitsentences [4]. A split sentence consists of up to sixmonths incarceration followed by five years probation.The probationers serve the jail portion of their sentenceunder domicile restriction.

START combines electronic monitoring with inten-sive supervision—office contact twice a week supple-mented by at least one field contact per week. Theprogram uses radio-frequency-controlled devices, pri-marily bracelets, for individuals confined to a single

location. Bracelets are not suit-able for other types of proba-tioners, such as juveniles whoattend school and adults whowork.

In 1999, START imple-mented text-prompted voice-based tracking for the first 30days of every START sentenceand began using the system asan alternative to electronicbracelets when a city courtimposes curfew on a proba-tioner lacking long-distancetelephone service.

Before probationers enter theprogram, START officers dis-cuss all conditions of probationwith them, including voicetracking. A description of thevoice tracking system appears inan addendum to The Condi-tions of Probation documentthe probationer is required to


Rough’n’Ready Browser

SearchesAudio

Archives

Speaker Turns Demarcated;

SpeakersIdentified

StoryBoundaries

Compose Searches

Time Lineof CurrentEpisode

Transcribed Speech,Named Entities in Color

TopicsIdentified;

StoriesSummarizedby Topics

Home Shopping Network is converting its touch-tone order-entry operation to speech recognition.

Figure 5. User interface for Rough’n’Ready, a broadcast news system.(The Rough’n’Ready system was developed at BBN/GTE Internet-

working [2] and acquired by Lernout & Hauspie Speech Products in1999; the interface here is being developed by Lernout & Hauspie.)

sign. The probationer is then enrolled in the voice-track-ing system under the supervision of a START officer.Most probationers are given pagers. The system ran-domly pages these probationers who must return thepage within 10 minutes. Some probationers, includingjuveniles, are required to call the voice-monitoring cen-ter at a given time and from a pre-specified location.Failure to return a page or make the scheduled call canresult in a violation of probation.

For probationers who stay in compliance for sixmonths, START reduces the level of monitoring byeliminating voice tracking. For probationers who vio-late probation, voice tracking is reinstituted.

START reports the voice system has worked so wellthat Family Court now uses the same technology tomonitor its curfews.

OutlookCurrent research and market trends indicate thatfuture applications of voice-biometrics will be text-independent and incorporate other speech-process-ing and biometric technologies. Such applicationsare already in demand in several markets. For exam-ple, health-care, financial services, and other indus-tries that handle large numbers of sensitivedocuments have begun to incorporate multiple bio-metrics into their security strategies. The use ofproducts for multiple and layered biometrics is fur-ther supported by declining prices on biometric sen-sors and development of standards, facilitating thedevelopment of multibiometric applications. InApril 2000, the BioAPI Consortium5 released ver-sion 1.0 of its BioAPI specification, and the follow-ing month, Microsoft announced its intention todevelop its own application programming interfacestandard.

The wireless industry, Internet security providers,and telecom services providers all support developmentof unobtrusive, text-independent speaker verificationand identification to secure the communications envi-ronments of the future. Some approaches focus onchip-based security embedded in wireless telephonesand PDAs. Other solutions require more powerfultechnology in communications networks, such as thework on continuous verification being done at the Uni-versity of Wales [1] and elsewhere. Other approaches

involve integration of speaker verification and otherbiometrics with public key infrastructure encryptionand digital certificates for securing e-commerce appli-cations.

Deployment of speech-recognition applicationsalso spurs demand for voice-based security. Compa-nies with existing speech-recognition applicationsare adding speaker verification as a way of extendingthese applications to secured transactions or asreplacements for PIN-based security. Moving in theother direction, HSN, for example, is converting itstouch-tone order-entry operation to speech recogni-tion, so members who voice-verify successfully canorder using just their voices. HSN is also weighingwhether to incorporate a second level of speaker ver-ification for large orders.

Developers are also applying the combined power oftext-independent speaker identification, speech recog-nition, and other technologies to the automation ofentirely new types of tasks. One such application is theautomatic indexing, search, and retrieval of informa-tion in audio sources, such as tape recordings and newsbroadcasts [8]. Figure 5 shows the user interface forRough’n’Ready, one of the systems described in [8].Developed by BBN technologies, Rough’n’Readyapplies a battery of speech-processing technologies tothe task of analyzing and indexing audio and videorecordings, such as news broadcasts.

These trends indicate acceptance of speaker verifica-tion and identification and that voice biometrics tech-nologies are increasingly viewed as components inlarger, more complex solutions.

References1. Auckenthaler, R., Carey, M., and Mason, J. Speaker-centric score normali-

sation and time pattern analysis for continuous speaker verification. In Pro-ceedings of ICASSP 2000, the IEEE International Conference on Acoustics,Speech, and Signal Processing (Istanbul, Turkey, June 5–9). IEEE Press, Pis-cataway, N.J., 2000, 48–50.

2. Kubala, F., Colbath, S., Liu, D., Srivastava, A., and Makhoul, J. Integratedtechnologies for indexing spoken language. Commun. ACM 43, 2 (Feb.2000), 48–56.

3. Markowitz, J. Ieri, oggi, domani: Speaker recognition yesterday, today, andtomorrow. In Proceedings of COST250 Workshop on Speacker Recognition inTelephony (Rome, Italy, Nov. 10). European Co-operation in the Field ofScientific and Technical Research, 1999.

4. Markowitz, J. Hands on with ... The New York Department of Probation.Voice ID Quart. 4, 2 (Apr. 2000).

5. Markowitz, J. Hands on with ... Home Shopping Network. Voice ID Quart.3, 4 (Oct. 1999).

6. Markowitz, J. Hands on with ... Girl Tech. Voice ID Quart. 3, 2 (Apr. 1999).7. Markowitz, J. Hands on with ... Illinois Department of Revenue. Voice ID

Quart. 2, 3 (Oct. 1998).8. Maybury, M. News on demand, special section. Commun. ACM 43, 2 (Feb.

2000), 32–79.

Judith Markowitz ([email protected]) is president of J.Markowitz Consultants in Evanston, IL and Chicago.

© 2000 ACM 0002-0782/00/0900 $5.00

c


5The BioAPI Consortium was formed in 1998 by Compaq Computer, IBM, Identica-tor Technology, Microsoft, Miros, and Novell (representing the Speaker VerificationAPI standard workgroup) for the purpose of developing a specification of a standard-ized API compatible with a wide range of biometrics application programs and bio-metrics technologies. Consortium members now also include biometrics vendors andconsultants (Identicator, IriScan, ITT Industries, J. Markowitz Consultants, Keyware,Mytec, National Biometric Test Center, and Visionics) and biometrics users (BarclaysBank, Intel, Kaiser Permanente, U.S. National Institute of Standards in Technology,and the U.S. National Security Agency.

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Voice Biometrics p66 Markowitz