UNITED STATES PATENT AND TRADEMARK OFFICE
`
`_________________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`
`_________________
`
`GOOGLE LLC,
`Petitioner,
`
`v.
`
`BUFFALO PATENTS, LLC,
`Patent Owner.
`
`_________________
`
`Case No. IPR2023-01387
`U.S. Patent No. 8,204,737
`_________________
`
`Declaration of Shauna L. Wiest Regarding Campbell
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`Page 1 of 51
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`GOOGLE EXHIBIT 1020
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`

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`Declaration of Shauna L. Wiest
`
`I, Shauna L. Wiest, state and declare as follows:
`
`I.
`
`Introduction
`
`1.
`
`I have prepared this Declaration in connection with Google LLC’s
`
`(“Petitioner”) Petition for Inter Partes Review of U.S. Patent Nos. 8,204,737
`
`which I understand will be filed concurrently with this Declaration.
`
`2.
`
`I am a senior research analyst with the Research & Information
`
`Services team at Finnegan, Henderson, Farabow, Garrett & Dunner, LLP
`
`located at 3300 Hillview Avenue, Palo Alto, CA 94304 (“Finnegan”).
`
`3.
`
`I am over eighteen years of age, and I am competent to make this
`
`Declaration. I make this Declaration based on my own personal knowledge,
`
`and my professional knowledge of library science practices.
`
`4.
`
`I earned a Master of Science in Library Science degree from the
`
`University of North Carolina at Chapel Hill in 1999, and a Bachelor of Arts in
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`Political Science degree from the University of California at San Diego in
`
`1989. I have worked as a law librarian for over eighteen years. I have been
`
`employed in the Research & Information Services Department at Finnegan
`
`since 2021. Before that, from 2000-2015, I was employed as a Law Librarian
`
`at Stoel Rives LLP, and from 2015-2016, I was employed as a Competitive
`
`Intelligence Specialist for Nossaman LLP.
`
`2
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`Declaration of Shauna L. Wiest
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`II.
`
`Standard Library Practices for Receiving, Cataloging, Shelving, and
`Making Materials Publicly Available
`
`5.
`
`I have knowledge of and experience with standard library practices
`
`regarding receiving, cataloging, shelving, and processes for making materials
`
`available to the public. I am fully familiar with the Machine-Readable Cataloging
`
`(“MARC”) system, an industry-wide standard that libraries worldwide use to
`
`catalog materials.
`
`6.
`
`The MARC record system was developed during the 1960s to
`
`standardize bibliographic catalog records so they could be read by computers and
`
`shared among libraries. By the mid-1970s, MARC had become the international
`
`standard for cataloging bibliographic materials and is still used today. Many
`
`libraries provide public access to their MARC records via the Internet and/or their
`
`electronic cataloging systems at the library. In a MARC record, each field provides
`
`specific information about the cataloged item, including how materials are held
`
`and when they were catalogued and made available to the public.
`
`III. MARC Record Field Tags
`
`7.
`
`The MARC record system uses a specific three-digit numeric code
`
`(“field tags”) (from 001-999) to identify each field in a catalog record. For
`
`example, MARC field tag 008 provides the six-digit date the item was received
`
`and catalogued (Date entered on file). The first six characters of field tag 008 are
`
`always in the “YYMMDD” format. Descriptions and definitions of all of the
`
`3
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`character positions of field tag 008 are outlined here:
`
`Declaration of Shauna L. Wiest
`
`
`https://www.loc.gov/marc/bibliographic/bd008a.html
`
`8.
`
`As is relevant to this Declaration, MARC field tag 022 provides the
`
`International Standard Serial Number (ISSN), a unique identification number
`
`assigned to a serial publication. MARC field tag 260 identifies the place of
`
`publication, name of publisher, and date of the publication. MARC field tag 362
`
`sets forth the dates of publication and/or sequential date(s) of publication
`
`designation for a work. MARC field tag 991 provide local holdings information to
`
`assist the public in identifying the location of a desired resource.
`
`9.
`
`Based on standard library practice, when a library receives an item,
`
`it generally stamps (and/or labels) the item with the library name, barcode,
`
`often with a date that is within a few days or weeks of receipt. Next, the library
`
`will catalog the item within a matter of a few days or weeks of receiving it. As
`
`a general practice, cataloguing is centralized and performed by a cataloguing
`
`department within a library or university setting. In certain circumstances the
`
`catalogued item may be subsequently sent to a library location within the
`
`library or university setting where it may be stamped and/or labeled after it has
`
`been catalogued.
`
`10. Generally, after an item is cataloged, the public may access the
`
`item by searching a library catalog, browsing the library shelves, and either
`
`
`
`4
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`Declaration of Shauna L. Wiest
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`requesting or electronically accessing the item from the library. Standard
`
`library practice is to make the item available to the public within a few days or
`
`weeks of cataloging it.
`
`IV.
`
`Serial Publications
`
`11. A serial publication, often known as a “journal,” is a resource that
`
`is issued in successive parts and has no predetermined conclusion. These
`
`successive parts are commonly referred to as “issues,” and each issue is usually
`
`chronologically numbered and dated. The presence of enumeration, years of
`
`coverage, and/or other chronological information, also indicates a serial
`
`publication.
`
`12.
`
`There are differences between cataloging finite resources
`
`(books/monographs) and continuing resources (serials). For serials, the catalog
`
`record provides information about the serial as a whole, including the first or
`
`earliest available issue. It also provides information as to holdings – the volumes
`
`and issues received and held by the library, including public availability of those
`
`holdings. In serials cataloging, there are identifying characteristics unique to serials
`
`that are slightly different from monographs (books). The issue date for a serial
`
`publication, for example, generally appears on the cover (front or back), the
`
`masthead page, the title page (if any), the table of contents page(s), or on the pages
`
`of the individual articles contained within the issue. More information regarding
`
`5
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`Declaration of Shauna L. Wiest
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`the unique aspects of cataloguing serials can be found at this link:
`
`https://www.loc.gov/aba/pcc/conser/scctppt/Basic-2014/Basic-Trainee-
`
`Mannual.pdf
`
`V.
`
`Public Availability of Campbell
`
`13.
`
`This Declaration relates to the date of public availability for the
`
`following reference: J. P. Campbell, “Speaker recognition: a tutorial, (An
`
`Invited Paper)" (“Campbell”) in Proceedings of the IEEE, vol. 85, no. 9, pp.
`
`1437-1462, September 1997 (“1997 IEEE Publication”). I understand that
`
`Campbell has been submitted as Exhibit 1010 in this proceeding. That same
`
`reference is appended to my Declaration as Appendix A.
`
`14. As detailed below, I have reviewed the print reference, public
`
`holdings information, and Library of Congress MARC record for Campbell to
`
`determine the date of public availability of this reference.
`
`15. Appendix A to this Declaration is a true and accurate copy of the
`
`print journal cover (including binding), title page, and table of contents for the
`
`issue of the 1997 IEEE Publication containing Campbell held by the Library of
`
`Congress. Appendix A also includes pages 1437-1462, which is the article titled
`
`“Speaker recognition: a tutorial, (An Invited Paper),” i.e. Campbell. The date
`
`stamp for this issue indicates that Campbell was received by the Library of
`
`Congress on September 23, 1997. The journal cover provides directional
`
`6
`
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`Declaration of Shauna L. Wiest
`
`information so that members of the interested public could access the print volume
`
`containing Campbell at Call Number TK5700 .I6.
`
`16. Appendix B to this Declaration is a true and correct copy of the
`
`Library of Congress public catalog record for its copy of 1997 IEEE Publication
`
`containing Campbell which was downloaded from https://lccn.loc.gov/86645263
`
`on August 18, 2023. The Library of Congress’s public catalog record for its copy
`
`of 1997 IEEE Publication containing Campbell sets forth the publication history
`
`and item availability including onsite holdings and onsite location information for
`
`members of the public seeking the print issue of 1997 IEEE Publication containing
`
`Campbell. The public catalog record indicates that the print issue for 1997 IEEE
`
`Publication containing Campbell should be requested in the Jefferson or Adams
`
`Building Reading Rooms within holdings v.82-v.108 (1994:Nov.-2020:Dec.) at
`
`Call Number TK5700 .I6. Based on my experience as a librarian, the public
`
`catalog record (Appendix B) references the 1997 IEEE Publication containing
`
`Campbell (Appendix A).
`
`17. Appendix C to this Declaration is a true and accurate copy of the
`
`Library of Congress MARC record for its holdings of the serial publication 1997
`
`IEEE Publication containing Campbell, which was downloaded from
`
`https://catalog.loc.gov/vwebv/staffView?searchId=20720&recPointer=0&recCount
`
`=25&searchType=2&bibId=11315346 on August 18, 2023. The Library of
`
`7
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`Declaration of Shauna L. Wiest
`
`Congress MARC record field tag 022 denotes the unique International Standard
`
`Serial Number (ISSN) for 1997 IEEE Publication as 0018-9219. MARC field tag
`
`260 denotes the place of publication, publisher, and publication date as: a [New
`
`York, N.Y.] : |b Institute of Electrical and Electronics Engineers, |c 1963-. MARC
`
`record field tag 362 denotes a chronological designation of a continuing (serial)
`
`resource. In this MARC record for the 1997 IEEE Publication, the sequential serial
`
`designation begins with Volume 51, no. 1 (Jan. 1963)- with no end date noted. And
`
`MARC field tag 991 set forth the local holdings, location, and call number
`
`information for The Library of Congress holdings for 1997 IEEE Publication
`
`containing Campbell. The MARC record denotes this information as: General
`
`Collection, Call Number TK5700 .I6 with holdings beginning with the issue
`
`Volume 64, Jan-Jun 1976.
`
`18. Appendix C confirms the fixed data elements of MARC field tag 008
`
`as 751101c19639999nyumrp0a0engc. As discussed above, the first six characters
`
`“751101” are in typical “YYMMDD” format and indicate that the serial
`
`publication 1997 IEEE Publication was initially catalogued by the Library of
`
`Congress on November 1, 1975. The publication status code “c1963” appearing in
`
`MARC field tag 008 refers to “continuing resource currently published,” indicating
`
`that the serial 1997 IEEE Publication began publication in 1963 and is a continuing
`
`published resource. Based on my experience as a librarian, the MARC record
`
`8
`
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`

`Declaration of Shauna L. Wiest
`
`(Appendix C) references the 1997 IEEE Publication containing Campbell
`
`(Appendix A).
`
`19. Based on the information in Appendices A, B and C, the issue of the
`
`1997 IEEE Publication containing Campbell was received by the Library of
`
`Congress on or around September 23, 1997. Based on standard library practices,
`
`Campbell would have been processed and catalogued by the Library of Congress
`
`within a matter of a few days or weeks of September 23, 1997.
`
`20. Accordingly, Campbell would have been made available to the public
`
`within a few days or weeks of being checked-in and catalogued. The public could
`
`have accessed Campbell by searching the Library of Congress catalog and
`
`requesting the item in the Newspaper & Current Periodical Reading Room
`
`(Madison LM133) within a few weeks of September 23, 1997.
`
`VI. Conclusion
`
`21.
`
`In signing this Declaration, I understand it will be filed as evidence
`
`in a contested case before the Patent Trial and Appeal Board of the United
`
`States Patent and Trademark Office. I understand I may be subject to cross-
`
`examination in this case and that cross-examination will take place within the
`
`United States. If cross-examination is required of me, I will appear for cross-
`
`examination within the United States during the time allotted for cross-
`
`examination.
`
`9
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`Declaration of Shauna L. Wiest
`
`22.
`
`I declare that all statements made herein of my knowledge are true,
`
`that all statements made on information and belief are believed to be true, and that
`
`these statements were made with the knowledge that willful false statements and
`
`the like so made are punishable by fine or imprisonment, or both, under Section
`
`1001 of Title 18 of the United States Code.
`
`Executed on September 11, 2023.
`
`Shauna L. Wiest
`
`10
`
`Page 10 of 51
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`

`

`APPENDIX A
`APPENDIX A
`
`Page 11 of 51
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`Page 11 of 51
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`

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`Page 12 of 51
`
`va3
`
`
`
`SUIIEERINE
`
`Bat)
`
`433
`
`L661
`
`Page 12 of 51
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`

`fi
`
`» THE INSTITUTE OF ELECTRICAL AND ELECTRONICS eenase Nex
`
`SEPTEMBER1997
`
`\
`
`y
`’
`
`*IrisRecognition
`
`THE PROCEEDINGS
`
`AEU
`aedHLmeet
`
`SOL
`
`+ Fingerprint Identity Authentication
`« Fingerprint Features
`EyHLA CARE ETO
`_
`+ Speaker Recognition
`+ EvaluationofIdentification ~
`and Verification Systems
`Sy ZENCLO SYA
`
`1915 Classic Paper:
`“The Pure Electron Discharge
`andIt’s Applicitnim
`Radio Telegraphy aud Telephony”
`by Irving Langmuir
`
`Scanning the Past: Ralbh Bown &
`The Golden Age ofPropagation Research
`
`EI
`
`Page 13 of 51
`
`Page 13 of 51
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`

`

`& IPIROCIEIEIDIINGS®TEEE
`
`
`
`published monthly by the Institute of Electrical and Electronics Engineers, Inc.
`September 1997 Vol. 85
`No. 9
`
`
`
`ear
`
`SEP 23 1997
` SPECIAL ISSUE ON
`
`AUTOMATED BIOMETRICS
`Edited by Weicheng Shen and Rajiv Khanna
`
`1343
`
`Scanning the Special Issue on Automated Biometrics, W. Shen and R. Khanna
`
`PAPERS
`
`1348
`
`1423
`
`1437
`
`Iris Recognition: An Emerging Biometric Technology, R. P. Wildes
`1347 Prolog, W. Shen and R. Khanna
`1365—AnIdentity-Authentication System Using Fingerprints
`A. K. Jain, L. Hong, S. Pankanti, and R. Bolle
`1364 Prolog, W. Shen and R. Khanna
`1390_—_—~Fingerprint Features—Statistical Analysis and System Performance Estimates
`(Invited Paper), A. R. Roddy and J. D. Stosz
`:
`1389 Prolog, W. Shen and R. Khanna
`Face Recognition: Eigenface, Elastic Matching, and Neural Nets
`(Invited Paper), J. Zhang, Y. Yan, and M. Lades
`1422 Prolog, W. Shen and R. Khanna
`Speaker Recognition: A Tutorial (Invited Paper), J. P. Campbell, Jr.
`1436 Prolog, W. Shen and R. Khanna
`1464~~Evaluation of Automated Biometrics-Based Identification and Verification Systems
`W. Shen, M. Surette, and R. Khanna
`
`
`
`
`
`VEVIVIVEDEDEDEDEDDE)ssevemecnoerere1]0]001TTTTTUTITTTTTTTTTPTPTETETEETETTTT
`
`Prolog, W. Shen and R. Khanna
`1463
`Biometrics: Privacy’s Foe or Privacy’s Friend? J. D. Woodward
`1479
`Prolog, W. Shen, R. Khanna, and J. D. Woodward
`Comments on “The Pure Electron Discharge and Its Applications in Radio Telegraphy
`and Telephony” (Invited Paper), C. K. Birdsall
`1496—-The Pure Electron Discharge and Its Applications in Radio Telegraphy and Telephony
`(Classic Paper), I. Langmuir
`
`1480
`
`1493
`
`BOOK REVIEWS
`
`1509
`
`The Balanced Scorecard: Translating Strategy into Action by R. S. Kaplan and
`D. P. Norton, Reviewed by R. C. Dorf and M. Rattanen
`
`SCANNING THE PAST
`
`1511
`1514.
`1516
`
`Ralph Bown and the Golden Age of Propagation Research, J. E. Brittain
`FUTURE SPECIAL ISSUES/SPECIAL SECTIONS OF THE PROCEEDINGS
`PROCEEDINGS CLASSIC PAPER REPRINT SCHEDULE
`
`Page 14 of 51
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`

`

`PROCEEDINGS OF ‘THE IEEE
`1997 EDITORIAL BOARD
`Richard B. Fair, Editor
`James E. Brittain, Associate Editor, History
`
`Winser E. Alexander
`Roger Barr
`Albert Benveniste
`G. M. Borsuk
`Bimal K, Bose
`Lawrence Carin
`Giovanni De Micheli
`Per Enge
`E. K. Gannett
`Erol Gelenbe
`T. G, Giallorenzi
`J. D. Gibson
`Bijan Jabbari
`Dwight L. Jaggard
`Peter Kaiser
`
`Sung-Mo(Steve) Kang
`Murat Kunt
`Chen-Ching Liu
`Massimo Maresca
`K. W. Martin
`Yale N. Patt
`Theo Pavlidis
`P. B. Schneck
`Marwan Simaan
`L. M. Terman
`Fawwaz T. Ulaby
`Paul P. Wang
`H. R. Wittmann
`Francis T. S. Yu
`
`1997 IEEE PUBLICATIONS BOARD
`Friedolf Smits, Chair
`Tariq S. Durrani, Vice Chair
`
`IEEE STAFF
`Daniel J. Senese, Executive Director
`
`Frederick T. Andrews
`David Daut
`Kenneth Dawson
`Stephen L. Diamond
`Richard B. Fair
`Gregg Gibson
`Roger Hoyt
`W. Dexter Johnston, Jr.
`Marcel Keschner
`
`Deborah Flaherty Kizer
`Prasad Kodali
`Frank Lord
`William Middleton
`Robert T. Nash
`Charles Robinson
`Allan C. Schell
`Steven Unger
`George W. Zobrist
`
`STAFF EXECUTIVES
`Anthony J. Ferraro, Publications
`Richard D. Schwartz, Business
`Administration
`
`MANAGING DIRECTORS
`Donald Curtis, Human Resources
`Cecelia Jankowski, RegionalActivities
`Peter A Lewis, Educational
`Activities
`Andrew G. Salem, Standards Activities
`W. Thomas Suttle,
`Professional Activities
`John Witsken,
`Information Technology
`
`PUBLICATIONS DIRECTORS
`
`Kenneth Moore, [EEE Press
`Lewis Moore,
`Publications Administration
`Fran Zappulla,
`Staff Director, IEEE Periodicals
`
`PROCEEDINGS STAFF
`
`Jim Calder, Managing Editor
`Margery Scanlon,
`Editorial Coordinator
`Gail S. Ferenc, Transactions Manager
`Valerie Cammarata, Edttorial Manager
`Geraldine E. Krolin,
`Managing Editor, TRANSACTIONS/JOURNALS
`Tonya Ugoretz Buzby, Associate Editor
`
`Frank Caruthers, Jim Esch,
`Howard Falk, Richard A. O’Donnell.
`Kevin Self, George Likourezos
`Contributing Editors
`Stephen Goldberg, Cover Artist
`Susan Schneiderman, Richard C. Faust,
`Advertising Sales
`
`Manuscripis should be submitted in triplicate to the Editor at the IEEE Operations Center. A
`summary ofinsiruclions for preparation is found in the most recent January issue ofthis journal.
`Detailed instructions are contained in “Information for IEEE Transactions and Journals Authors,"
`available on request, After a manuscript has been accepted for publication, the author's organi-
`zation will be requested (o honor a charge of $110 per printed page (one-page minimum charge) to
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`Copyright: [t is the policy of the [EEE to own the copyright to the technical contributionsit
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`appropriate reuse ofthis matcrial by others. To comply with the U.S. Copyright Law, authors are
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`for the following month’s issue, Send new address, plus mailing label showing old address, to the
`PROCEEDINGS OF THE [EEE (ISSN 0018-9219; cudes IREPAD)is published monthly by the
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`Institute of Electrical and Electronics Engineers, Inc.
`IEEE Corporate Office: 345 Eas! 47th
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`CONTRIBUTIONS
`
`The PROCEEDINGS OF THE IEEE publishes comprehensive, in-depth
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`The papers are of long-range interest and broad significance. Applications
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`
`COVER Thisspecial issue covers the subject of automated biometric systemsthat are used to verify individual identity using unique biometric mcasurements
`of the human body, Our coverillustrates the concept of one of the growing number of applications of these systems.
`
`Page 15 of 51
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`This material may be protected by Copyright law (Title 17 U.S. Code)
`
`Speaker Recognition: A Tutorial
`
`
`
`JOSEPH P. CAMPBELL, JR., SENIOR MEMBER,IEEE
`
`Invited Paper
`
`A tutorial on the design and development of automatic speaker-
`recognition systems is presented. Automatic speaker recognition
`is the use of a machine to recognize a person from a spoken
`phrase. These systems can operate in two modes:
`to identify
`a particular person or to verify a person’s claimed identity.
`Speech processing and the basic components ofautomatic speaker-
`recognition systems are shownand designtradeoffs are discussed.
`Then, a new automatic speaker-recognition system is given. This
`recognizer performs with 98.9% correct identification, Last,
`the
`performances of various systems are conipared.
`Keywords—Accesscontrol, authentication, biomedical measure-
`ments, biomedical signal processing, biomedical transducers, bio-
`metric, communication system security, computer network security,
`computer security, corpus, data bases,
`identification of persons,
`public safety, site security monitoring, speaker recognition, speech
`processing, verification.
`
`I.
`
`INTRODUCTION
`
`In keeping with this special issue on biometrics, the focus
`of this paper is on facilities and network access-control
`applications of speaker recognition. Speech processing is a
`diverse field with many applications. Fig.
`1 shows a few of
`these areas and how speaker recognition relates to the rest
`ofthe field; this paper focuses on the three boxed areas.
`Speaker recognition encompasses verification and iden-
`tification. Automatic speaker verification (ASV)is the use
`of a machine to verify a person’s claimed identity from
`his voice. The literature abounds with different terms for
`speaker verification, including voice verification, speaker
`authentication, voice authentication, talker authentication,
`and talker verification. In automatic speaker identification
`(ASI), there is no a priori identity claim, and the system
`decides who the person is, what group the person is a
`member of, or (in the open-set case) that the person is
`unknown. General overviews of speaker recognition have
`been given in [2], [12], [17], [37], [51], [52], and [59].
`Speakerverification is defined as deciding if a speakeris
`whom he claims to be. This is different than the speaker
`
`Manuscript received April 20, 1997; revised June 27, 1997.
`The author is with the National Security Agency, R22, Ft. Meade,
`MD 20755-6516 USA;
`and the Whiting School of Engineering,
`The Johns Hopkins University, Baltimore, MD 21218 USA (e-mail:
`j.campbell @ieee.org).
`Publisher Item Identifier $ 0018-92 19(97)06947-8.
`
`Speech Provessing
`
`Analysis/Synthesis
`
`Recognition
`
`Coding
`
`Speech
`Recognition
`
`Speaker
`Recognition
`
`Language
`Identification
`
`Speaker
`Identification
`
`Speaker
`Detection
`
`Speaker
`Verification
`
`Text
`Independent
`Unwitting
`Speakers
`Variable
`Quality
`Speech
`
`Text
`Independent
`Cooperative
`Speakers
`High
`Quality
`Speech
`
`Speech
`
`Text
`Independent
`Cooperative
`Speakers
`High
`Quality
`Speech
`
`Text
`Dependent
`Cooperative
`Speakers
`High
`Quality
`
`Fig. 1. Speech processing.
`
`™
`
`identification problem, which is deciding if a speaker is a
`specific person or is among a group of persons. In speaker
`verification, a person makes an identity claim (e.g., by
`entering an employee numberor presenting his smart card).
`In text-dependent recognition, the phrase is known to the
`system and can be fixed or prompted (visually or orally).
`The claimant speaks the phrase into a microphone. This
`signal is analyzed by a verification system that makes the
`binary decision to accept or reject the user’s identity claim
`or possibly to report
`insufficient confidence and request
`additional input before making the decision.
`A typical ASV setup is shown in Fig. 2. The claimant,
`who has previously enrolled in the system, presents an
`encrypted smart card containing his identification informa-
`tion. He then attempts to be authenticated by speaking a
`prompted phrase(s) into the microphone. There is generally
`a tradeoff between accuracy andtest-session duration. In
`addition to his voice, ambient room noise and delayed
`versions of his voice enter the microphone via reflective
`acoustic surfaces. Prior to a verification session, users must
`enroll in the system (typically under supervised conditions).
`During this enrollment, voice models are generated and
`stored (possibly on a smart card) for use in later verification
`
`PROCEEDINGS OF THE IEEE, VOL. 85, NO. 9, SEPTEMBER 1997
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`1437
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`U.S. Government work not protected by U.S. copyright.
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`
`
`
`Pattern
`
`Feature
`|_Digital
`Filtering
`and A/D|Speech”|Extraction
`Matching|Scores
`
`
`
`
`
`NEARS Taconic
`
`Doe SS . | Surface Microphone
`
` © _ Microphone
`
`Claimed ID
`
`Fig. 3. Generic speaker-verification system.
`
`B. Problem Formulation
`
`Speech is a complicated signal produced as a result
`of several
`transformations occurring at several different
`levels: semantic, linguistic, articulatory, and acoustic. Dif-
`ferences in these transformations appear as differences in
`the acoustic properties of the speech signal. Speaker-related
`differences are a result of a combination of anatomical
`differences inherent in the vocal tract and the learned speak-
`ing habits of different individuals. In speaker recognition,
`all these differences can be used to discriminate between
`speakers.
`
`C. Generic Speaker Verification
`The general approach to ASV consists of five steps:
`digital speech data acquisition, feature extraction, pattern
`matching, making an accept/reject decision, and enrollment
`to generate speaker reference models. A block diagram
`of this procedure is shown in Fig. 3. Feature extraction
`maps eachinterval of speech to a multidimensional feature
`space. (A speech interval typically spans 10-30 msof the
`speech waveform andis referred to as a frame of speech.)
`This sequence of feature vectors 2; is then compared to
`speaker models by pattern matching. This results in a match
`score 2; for each vector or sequence of vectors. The match
`score measures the similarity of the computed input feature
`vectors to models of the claimed speaker or feature vector
`patterns for the claimed speaker. Last, a decision is made to
`either accept or reject the claimant according to the match
`score or sequence of match scores, which is a hypothesis-
`testing problem.
`For speakerrecognition, features that exhibit high speaker
`discrimination power, high interspeaker variability, and
`low intraspeaker variability are desired. Many forms of
`pattern matching and corresponding models are possible.
`Pattern-matching methods include dynamic time warping
`(DTW), the hidden Markov model (HMM),artificial neural
`networks, and vector quantization (VQ). Template models
`are used in DTW,statistical models are used in HMM,and
`codebook models are used in VQ.
`
`D. Overview
`
`The purpose of this introductory section is to present a
`general framework and motivation for speaker recognition,
`an overview of the entire paper, and a presentation of
`previous work in speaker recognition.
`Section II contains an overview of speech processing,
`including speech signal acquisition,
`the data base used
`in later experiments, speech production, linear prediction
`(LP),
`transformations,
`and the cepstrum. Section TI
`
`PROCEEDINGS OF THE IEEE, VOL. 85, NO. 9, SEPTEMBER 1997
`
`Smart Card
`
`(Ambient Noise
`
`Fig. 2. Typical speaker-verification setup.
`
`
`
`Table 1 Sources of Verification Error
`
`Misspoken or misread prompted phrases
`
`Extreme emotionalstates (e.g., stress or duress)
`
`Time varying(intra- or intersession) microphone placement
`
`Poor or inconsistent room acoustics(e.g., multipath and noise)
`
`Channel mismatch(e.g., using different microphones for
`enrollment and verification)
`
`Sickness(e.g., head colds can alter the vocal tract)
`
`Aging (the vocal tract can drift away from models with age)
`
`sessions. There is also generally a tradeoff between accu-
`tacy and the duration and number of enrollmentsessions.
`Many factors can contribute to verification and iden-
`tification errors. Table 1
`lists some of the human and
`environmental factors that contribute to these errors, a few
`of which are shown in Fig. 2. These factors generally are
`outside the scope of algorithms or are better corrected by
`means other than algorithms (e.g., better microphones).
`These factors are important, however, because no matter
`how good a speaker-recognition algorithm is, human er-
`ror (e.g., misreading or misspeaking) ultimately limits its
`performance.
`
`A. Motivation
`
`ASV andASI are probably the most natural and econom-
`ical methods for solving the problems of unauthorized use
`of computer and communications systems and multilevel
`access control. With the ubiquitous telephone network and
`microphones bundled with computers, the cost of a speaker-
`recognition system might only be for software.
`Biometric systems automatically recognize a person by
`using distinguishing traits (a narrow definition). Speaker
`recognition is a performance biometric, i.e., you perform
`a task to be recognized. Your voice, like other biometrics,
`cannot be forgotten or misplaced, unlike knowledge-based
`(e.g., password) or possession-based (e.g., key) access-
`control methods. Speaker-recognition systems can be made
`somewhatrobust against noise and channelvariations [33],
`[49], ordinary human changes (e.g.,
`time-of-day voice
`changes and minor head colds), and mimicry by humans
`and tape recorders [22].
`
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`the divergence measure, and
`presents feature selection,
`the Bhattacharyya distance. This section is highlighted
`by the development of the divergence shape measure and
`the Bhattacharyya distance shape. Section IV introduces
`pattern matching and Section V presents classification,
`decision theory, and receiver operating characteristic
`(ROC) curves. Section VI describes a simple but effective
`speaker-recognition algorithm. Section VII demonstrates
`the performanceof various speaker-recognition algorithms,
`and Section VIII concludes by summarizing this paper.
`
`E. Previous Work
`
`There is considerable speaker-recognition activity in in-
`dustry,