US20150006397A1 - System and Method for Tracking Fraudulent Electronic Transactions Using Voiceprints of Uncommon Words - Google Patents

System and Method for Tracking Fraudulent Electronic Transactions Using Voiceprints of Uncommon Words Download PDF

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Publication number
US20150006397A1
US20150006397A1 US14/481,348 US201414481348A US2015006397A1 US 20150006397 A1 US20150006397 A1 US 20150006397A1 US 201414481348 A US201414481348 A US 201414481348A US 2015006397 A1 US2015006397 A1 US 2015006397A1
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Prior art keywords
database
voice
voice signal
personal information
computer
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US14/481,348
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Mazin Gilbert
Jay Wilpon
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Nuance Communications Inc
AT&T Properties LLC
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AT&T Corp
AT&T Intellectual Property II LP
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Priority to US14/481,348 priority Critical patent/US20150006397A1/en
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Publication of US20150006397A1 publication Critical patent/US20150006397A1/en
Assigned to AT&T CORP. reassignment AT&T CORP. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: GILBERT, MAZIN, WILPON, JAY
Assigned to AT&T PROPERTIES, LLC reassignment AT&T PROPERTIES, LLC ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: AT&T CORP.
Assigned to AT&T INTELLECTUAL PROPERTY II, L.P. reassignment AT&T INTELLECTUAL PROPERTY II, L.P. CORRECTIVE ASSIGNMENT TO CORRECT THE ASSIGNORS DATA PREVIOUSLY RECORDED ON REEL 033955 FRAME 0841. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT. Assignors: AT&T PROPERTIES, LLC
Assigned to NUANCE COMMUNICATIONS, INC. reassignment NUANCE COMMUNICATIONS, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: AT&T INTELLECTUAL PROPERTY II, L.P.
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00Payment architectures, schemes or protocols
    • G06Q20/38Payment protocols; Details thereof
    • G06Q20/40Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00Payment architectures, schemes or protocols
    • G06Q20/38Payment protocols; Details thereof
    • G06Q20/382Payment protocols; Details thereof insuring higher security of transaction
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00Speech recognition
    • G10L15/08Speech classification or search
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L17/00Speaker identification or verification
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00Speech recognition
    • G10L15/02Feature extraction for speech recognition; Selection of recognition unit
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04MTELEPHONIC COMMUNICATION
    • H04M3/00Automatic or semi-automatic exchanges
    • H04M3/38Graded-service arrangements, i.e. some subscribers prevented from establishing certain connections

Definitions

  • the present invention relates generally to transaction fraud detection and more specifically to detecting credit card or other fraud by comparing voice samples to a database of known fraudulent voice signatures.
  • An exemplary method embodiment comprises comparing a received voice signal against a database of known fraudulent voice signatures, denying the caller's transaction if the voice signal substantially matches the database of known fraudulent voice signatures, and adding the caller's voice signal to the database of known fraudulent voice signatures if the voice signal does not substantially match a separate speaker verification database and received additional information is not verified.
  • the principles of the invention may be utilized to provide an additional layer of security for credit card transactions through voice recognition. In this way, repeat fraudsters are prevented from committing additional fraud because their voices are identified and added to the database of known fraudulent voice signatures.
  • FIG. 1 illustrates a basic system or computing device embodiment of the invention
  • FIG. 2 illustrates an exemplary system for detecting fraudulent transactions using voice data to continually update a database of known fraudulent voice signatures.
  • FIG. 3 illustrates a method embodiment of the invention.
  • an exemplary system for implementing the invention includes a general-purpose computing device 100 , including a processing unit (CPU) 120 and a system bus 110 that couples various system components including the system memory such as read only memory (ROM) 140 and random access memory (RAM) 150 to the processing unit 120 .
  • system memory 130 may be available for use as well.
  • the system bus 110 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures.
  • the computing device 100 further includes storage means such as a hard disk drive 160 , a magnetic disk drive, an optical disk drive, tape drive or the like.
  • the storage device 160 is connected to the system bus 110 by a drive interface.
  • the drives and the associated computer readable media provide nonvolatile storage of computer readable instructions, data structures, program modules and other data for the computing device 100 .
  • the basic components are known to those of skill in the art and appropriate variations are contemplated depending on the type of device, such as whether the device is a small, handheld computing device, a desktop computer, or a computer server.
  • an input device 190 represents any number of input mechanisms, such as a microphone for speech, a touch sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech and so forth.
  • the input may be used by the presenter to indicate the beginning of a speech search query.
  • the device output 170 can also be one or more of a number of output means.
  • multimodal systems enable a user to provide multiple types of input to communicate with the computing device 100 .
  • the communications interface 180 generally governs and manages the user input and system output. There is no restriction on the invention operating on any particular hardware arrangement and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed.
  • the illustrative embodiment of the present invention is presented as comprising individual functional blocks (including functional blocks labeled as a “processor”).
  • the functions these blocks represent may be provided through the use of either shared or dedicated hardware, including, but not limited to, hardware capable of executing software.
  • the functions of one or more processors presented in FIG. 1 may be provided by a single shared processor or multiple processors.
  • Illustrative embodiments may comprise microprocessor and/or digital signal processor (DSP) hardware, read-only memory (ROM) for storing software performing the operations discussed below, and random access memory (RAM) for storing results.
  • DSP digital signal processor
  • ROM read-only memory
  • RAM random access memory
  • VLSI Very large scale integration
  • the present invention relates to detecting fraudulent transactions using voice data to continually update a database of known fraudulent voice signatures.
  • One preferred example and application in which the present invention may be applied is securing credit card transactions on the Internet.
  • FIG. 2 illustrates a basic example of an implementation of the method. Credit card users would be solicited for a voice pattern to add to the speaker verification database ( 202 ) prior to transactions in order to populate the speaker verification database. The speaker verification database might associate the voice pattern to the credit card number for easy lookup.
  • the user sends a voice signal ( 208 ) to the server ( 204 ).
  • this voice signal could be sent via VoIP from a web browser, a phone call to a server or call center, a phone call from the server, or any other method of transmitting and receiving a voice signal.
  • the server compares the received voice signal ( 208 ) to the database of known fraudulent voice signatures ( 206 ). If the voice signal is a substantial match to the database of known fraudulent voice signatures ( 206 ), the transaction is denied. If, on the other hand, there is a less than substantial match or no match at all, the voice signal ( 208 ) is compared to the speaker verification database ( 202 ).
  • the server may solicit additional information, for example, by asking “What is your mother's maiden name?” or “Please enter your pin number.” If the received information is not verified, the transaction is rejected and the user's voice signal is added to the database of known fraudulent voice signatures ( 206 ). In this way the database of known fraudulent voice signatures ( 206 ) is updated.
  • the method comprises comparing a received voice signal against a database of known fraudulent voice signatures ( 302 ), denying the caller's transaction if the voice signal substantially matches the database of known fraudulent voice signatures ( 304 ), and adding the caller's voice signal to the database of known fraudulent voice signatures if the voice signal does not substantially match a separate speaker verification database and received additional information is not verified ( 306 ).
  • One exemplary variation is where the system solicits a specific word or phrase to apply to both the database of known fraudulent voice signatures and the speaker verification database. For example, the system could prompt the caller: “Please state your name.” The resulting voice signal could be used to search both databases. This variation has the advantage of shorter speaking time for the caller and simultaneous lookup in both databases for faster overall processing time.
  • the system solicits a different word or phrase to apply to both databases.
  • the system could prompt the caller “Please say the phrase ‘I am not a crook’” to check the received voice signal for a substantial match in the database of known fraudulent voice signatures. Then the system could prompt the caller “Please say the word ‘prestidigitation’” or any other unusual or uncommonly spoken word and check the resulting voice signal for a match in the speaker verification database.
  • This approach has the advantage of allowing different voice signals to compare to each database. If needed, each phrase could supply additional material for comparison to the databases. In this way, each database comparison may be tailored to accommodate the received voice signals. In this regard, words may be used to tailor the test based on which database is being queried.
  • the first prompt may employ such word patterns for the verification against the fraudulent voice database.
  • the system may prompt the user to say: “I forgot my password” or some other phrase commonly or likely used by a fraudulent user. If particular word patterns are used by nonfraudulent people, then the second prompt may request word patterns like “My name is . . . ” to compare to the speaker verification database.
  • the system solicits a word or phrase to apply to the databases of known fraudulent voice signatures and some piece of personal information to apply to the speaker verification database.
  • the system could prompt the caller “Please say the phrase ‘I am not a crook’” to check the received voice signal for a substantial match in the database of known fraudulent voice signatures.
  • the system could prompt the caller “Please say your PIN number,” “Please state your name,” or “please say your mother's maiden name” and check the resulting voice signal against the speaker verification database.
  • This approach has the advantage of allowing custom, personal voice signals to compare to the speaker verification database. Voice signals containing personal information may have additional inflections or nuances to them which can aid in identifying fraudulent voice signals.
  • Yet another variation upon passing the first comparison to the database of known fraudulent voice signatures and not passing the second comparison to the speaker verification database, would transfer the call to an actual person who could solicit additional personal information, such as PIN number, billing address, or Card Security Code (such as the 3 digit code on the back of a Visa or Mastercard or the 4 digit code on the front of an American Express card) to verify that the caller's identity and authorize the transaction.
  • PIN number such as the 3 digit code on the back of a Visa or Mastercard or the 4 digit code on the front of an American Express card
  • One advantage over solutions known in the art is that a continually updated database of known fraudulent voice signatures prevents known imposters from stealing more cards. Once a “bad” voice signature is in the database that unauthorized voice cannot be used in future credit card transactions. Currently known or future discovered methods may be employed to correct and remove erroneous fraudulent voices from the fraudulent voice database. A dynamically updated database of known fraudulent voice signatures creates an additional layer of security and deterrence against repeat would-be offenders.
  • a caller's voice signal preferably substantially matches the database of known fraudulent voice signatures ( 206 ).
  • the concept of a substantial match is a dynamic one which should not be strictly interpreted as a rigid percentage or threshold. As an example, if a voice signature is common and similar to many others, a higher certainty may be necessary to establish a substantial match, whereas a unique or uncommon voice signature may require a lower certainty to establish a substantial match.
  • Another example is the relationship between the database of known fraudulent voice signatures and the speaker verification database ( 202 ).
  • the threshold of certainty could be raised when comparing to the speaker verification database, whereas if the voice print has a very low certainty match in the database of fraudulent voice signatures, then the threshold of certainty when comparing to the speaker verification database could be lowered. Finding a substantial match can mean a dynamic, sliding scale of certainty based on factors that may include the type of voice and the level of certainty of the match in the fraudulent voice signatures database ( 206 ) or speaker verification database ( 202 ).
  • Embodiments within the scope of the present invention may also include tangible computer-readable media for carrying or having computer-executable instructions or data structures stored thereon.
  • Such computer-readable media can be any available media that can be accessed by a general purpose or special purpose computer.
  • Such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code means in the form of computer-executable instructions or data structures.
  • a network or another communications connection either hardwired, wireless, or combination thereof
  • any such connection is properly termed a computer-readable medium. Combinations of the above should also be included within the scope of the computer-readable media.
  • Computer-executable instructions include, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions.
  • Computer-executable instructions also include program modules that are executed by computers in stand-alone or network environments.
  • program modules include routines, programs, objects, components, and data structures, etc. that perform particular tasks or implement particular abstract data types.
  • Computer-executable instructions, associated data structures, and program modules represent examples of the program code means for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represents examples of corresponding acts for implementing the functions described in such steps.
  • Embodiments of the invention may be practiced in network computing environments with many types of computer system configurations, including personal computers, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, and the like. Embodiments may also be practiced in distributed computing environments where tasks are performed by local and remote processing devices that are linked (either by hardwired links, wireless links, or by a combination thereof) through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Abstract

Disclosed are systems, methods, and computer-readable media for comparing customer voice prints comprising of uncommonly spoken words with a database of known fraudulent voice signatures and continually updating the database to decrease the risk of identity theft. The method embodiment comprises comparing a received voice signal against a database of known fraudulent voice signatures, denying the caller's transaction if the voice signal substantially matches the database of known fraudulent voice signatures, adding the caller's voice signal to the database of known fraudulent voice signatures if the voice signal does not substantially match a separate speaker verification database and received additional information is not verified.

Description

    PRIORITY INFORMATION
  • The present application is a continuation of U.S. patent application Ser. No. 11/754,800, filed May 29, 2007, the contents of which is incorporated herein by reference in its entirety.
  • BACKGROUND OF THE INVENTION
  • 1. Field of the Invention
  • The present invention relates generally to transaction fraud detection and more specifically to detecting credit card or other fraud by comparing voice samples to a database of known fraudulent voice signatures.
  • 2. Introduction
  • Currently, many users complete transactions on the Internet with credit cards. Many other users hesitate to use credit cards on the Internet for fear of identity theft. Identity theft is when an imposter attempts to use another's personal information to complete a transaction, for example trying to purchase a DVD boxed set from Amazon.com using stolen credit card information. Because online transactions are not conducted face to face, many card-based methods, such as printing a photograph of the cardholder on the card itself, have failed to adequately protect from identity theft in the eyes of consumers and the media. Although the risks of identity theft are generally no greater on the Internet than in other settings, the public perception is that the Internet is a risky place for credit card transactions. This public perception slows commerce and prevents companies from generating billions of dollars of new revenue. A small percentage of repeat offenders can encourage the public perception of the Internet as a risky place for commerce. Accordingly, what is needed in the art is a way to improve fraud detection and increase public trust in electronic commerce.
  • SUMMARY OF THE INVENTION
  • Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The features and advantages of the invention may be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the present invention will become more fully apparent from the following description and appended claims, or may be learned by the practice of the invention as set forth herein.
  • Disclosed herein are systems, methods, and computer readable media for detecting fraudulent transactions using voice data to continually update a database of known fraudulent voice signatures. An exemplary method embodiment comprises comparing a received voice signal against a database of known fraudulent voice signatures, denying the caller's transaction if the voice signal substantially matches the database of known fraudulent voice signatures, and adding the caller's voice signal to the database of known fraudulent voice signatures if the voice signal does not substantially match a separate speaker verification database and received additional information is not verified.
  • The principles of the invention may be utilized to provide an additional layer of security for credit card transactions through voice recognition. In this way, repeat fraudsters are prevented from committing additional fraud because their voices are identified and added to the database of known fraudulent voice signatures.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • In order to describe the manner in which the above-recited and other advantages and features of the invention can be obtained, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. Understanding that these drawings depict only typical embodiments of the invention and are not therefore to be considered to be limiting of its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings in which:
  • FIG. 1 illustrates a basic system or computing device embodiment of the invention;
  • FIG. 2 illustrates an exemplary system for detecting fraudulent transactions using voice data to continually update a database of known fraudulent voice signatures.; and
  • FIG. 3 illustrates a method embodiment of the invention.
  • DETAILED DESCRIPTION OF THE INVENTION
  • Various embodiments of the invention are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without parting from the spirit and scope of the invention.
  • With reference to FIG. 1, an exemplary system for implementing the invention includes a general-purpose computing device 100, including a processing unit (CPU) 120 and a system bus 110 that couples various system components including the system memory such as read only memory (ROM) 140 and random access memory (RAM) 150 to the processing unit 120. Other system memory 130 may be available for use as well. It can be appreciated that the invention may operate on a computing device with more than one CPU 120 or on a group or cluster of computing devices networked together to provide greater processing capability. The system bus 110 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. A basic input/output (BIOS), containing the basic routine that helps to transfer information between elements within the computing device 100, such as during start-up, is typically stored in ROM 140. The computing device 100 further includes storage means such as a hard disk drive 160, a magnetic disk drive, an optical disk drive, tape drive or the like. The storage device 160 is connected to the system bus 110 by a drive interface. The drives and the associated computer readable media provide nonvolatile storage of computer readable instructions, data structures, program modules and other data for the computing device 100. The basic components are known to those of skill in the art and appropriate variations are contemplated depending on the type of device, such as whether the device is a small, handheld computing device, a desktop computer, or a computer server.
  • Although the exemplary environment described herein employs the hard disk, it should be appreciated by those skilled in the art that other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, digital versatile disks, cartridges, random access memories (RAMs), read only memory (ROM), a cable or wireless signal containing a bit stream and the like, may also be used in the exemplary operating environment.
  • To enable user interaction with the computing device 100, an input device 190 represents any number of input mechanisms, such as a microphone for speech, a touch sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech and so forth. The input may be used by the presenter to indicate the beginning of a speech search query. The device output 170 can also be one or more of a number of output means. In some instances, multimodal systems enable a user to provide multiple types of input to communicate with the computing device 100. The communications interface 180 generally governs and manages the user input and system output. There is no restriction on the invention operating on any particular hardware arrangement and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed.
  • For clarity of explanation, the illustrative embodiment of the present invention is presented as comprising individual functional blocks (including functional blocks labeled as a “processor”). The functions these blocks represent may be provided through the use of either shared or dedicated hardware, including, but not limited to, hardware capable of executing software. For example the functions of one or more processors presented in FIG. 1 may be provided by a single shared processor or multiple processors. (Use of the term “processor” should not be construed to refer exclusively to hardware capable of executing software.) Illustrative embodiments may comprise microprocessor and/or digital signal processor (DSP) hardware, read-only memory (ROM) for storing software performing the operations discussed below, and random access memory (RAM) for storing results. Very large scale integration (VLSI) hardware embodiments, as well as custom VLSI circuitry in combination with a general purpose DSP circuit, may also be provided.
  • The present invention relates to detecting fraudulent transactions using voice data to continually update a database of known fraudulent voice signatures. One preferred example and application in which the present invention may be applied is securing credit card transactions on the Internet. FIG. 2 illustrates a basic example of an implementation of the method. Credit card users would be solicited for a voice pattern to add to the speaker verification database (202) prior to transactions in order to populate the speaker verification database. The speaker verification database might associate the voice pattern to the credit card number for easy lookup. When transactions are initiated, the user sends a voice signal (208) to the server (204). As an example this voice signal could be sent via VoIP from a web browser, a phone call to a server or call center, a phone call from the server, or any other method of transmitting and receiving a voice signal. The server compares the received voice signal (208) to the database of known fraudulent voice signatures (206). If the voice signal is a substantial match to the database of known fraudulent voice signatures (206), the transaction is denied. If, on the other hand, there is a less than substantial match or no match at all, the voice signal (208) is compared to the speaker verification database (202). If the voice signal does not substantially match the speaker verification database, then the server may solicit additional information, for example, by asking “What is your mother's maiden name?” or “Please enter your pin number.” If the received information is not verified, the transaction is rejected and the user's voice signal is added to the database of known fraudulent voice signatures (206). In this way the database of known fraudulent voice signatures (206) is updated.
  • This exemplary method embodiment of the invention is shown in FIG. 3. The method comprises comparing a received voice signal against a database of known fraudulent voice signatures (302), denying the caller's transaction if the voice signal substantially matches the database of known fraudulent voice signatures (304), and adding the caller's voice signal to the database of known fraudulent voice signatures if the voice signal does not substantially match a separate speaker verification database and received additional information is not verified (306).
  • One exemplary variation is where the system solicits a specific word or phrase to apply to both the database of known fraudulent voice signatures and the speaker verification database. For example, the system could prompt the caller: “Please state your name.” The resulting voice signal could be used to search both databases. This variation has the advantage of shorter speaking time for the caller and simultaneous lookup in both databases for faster overall processing time.
  • Another exemplary variation is where the system solicits a different word or phrase to apply to both databases. For example, the system could prompt the caller “Please say the phrase ‘I am not a crook’” to check the received voice signal for a substantial match in the database of known fraudulent voice signatures. Then the system could prompt the caller “Please say the word ‘prestidigitation’” or any other unusual or uncommonly spoken word and check the resulting voice signal for a match in the speaker verification database. This approach has the advantage of allowing different voice signals to compare to each database. If needed, each phrase could supply additional material for comparison to the databases. In this way, each database comparison may be tailored to accommodate the received voice signals. In this regard, words may be used to tailor the test based on which database is being queried. For example, if particular word patterns are used by fraudulent people, then the first prompt may employ such word patterns for the verification against the fraudulent voice database. The system may prompt the user to say: “I forgot my password” or some other phrase commonly or likely used by a fraudulent user. If particular word patterns are used by nonfraudulent people, then the second prompt may request word patterns like “My name is . . . ” to compare to the speaker verification database.
  • Another exemplary variation is where the system solicits a word or phrase to apply to the databases of known fraudulent voice signatures and some piece of personal information to apply to the speaker verification database. For example, the system could prompt the caller “Please say the phrase ‘I am not a crook’” to check the received voice signal for a substantial match in the database of known fraudulent voice signatures. Then the system could prompt the caller “Please say your PIN number,” “Please state your name,” or “please say your mother's maiden name” and check the resulting voice signal against the speaker verification database. This approach has the advantage of allowing custom, personal voice signals to compare to the speaker verification database. Voice signals containing personal information may have additional inflections or nuances to them which can aid in identifying fraudulent voice signals.
  • Yet another variation, upon passing the first comparison to the database of known fraudulent voice signatures and not passing the second comparison to the speaker verification database, would transfer the call to an actual person who could solicit additional personal information, such as PIN number, billing address, or Card Security Code (such as the 3 digit code on the back of a Visa or Mastercard or the 4 digit code on the front of an American Express card) to verify that the caller's identity and authorize the transaction.
  • One advantage over solutions known in the art is that a continually updated database of known fraudulent voice signatures prevents known imposters from stealing more cards. Once a “bad” voice signature is in the database that unauthorized voice cannot be used in future credit card transactions. Currently known or future discovered methods may be employed to correct and remove erroneous fraudulent voices from the fraudulent voice database. A dynamically updated database of known fraudulent voice signatures creates an additional layer of security and deterrence against repeat would-be offenders.
  • A caller's voice signal preferably substantially matches the database of known fraudulent voice signatures (206). The concept of a substantial match is a dynamic one which should not be strictly interpreted as a rigid percentage or threshold. As an example, if a voice signature is common and similar to many others, a higher certainty may be necessary to establish a substantial match, whereas a unique or uncommon voice signature may require a lower certainty to establish a substantial match. Another example is the relationship between the database of known fraudulent voice signatures and the speaker verification database (202). If the voice print has a medium or high certainty match in the database of fraudulent voice signatures, then the threshold of certainty could be raised when comparing to the speaker verification database, whereas if the voice print has a very low certainty match in the database of fraudulent voice signatures, then the threshold of certainty when comparing to the speaker verification database could be lowered. Finding a substantial match can mean a dynamic, sliding scale of certainty based on factors that may include the type of voice and the level of certainty of the match in the fraudulent voice signatures database (206) or speaker verification database (202).
  • Embodiments within the scope of the present invention may also include tangible computer-readable media for carrying or having computer-executable instructions or data structures stored thereon. Such computer-readable media can be any available media that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code means in the form of computer-executable instructions or data structures. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or combination thereof) to a computer, the computer properly views the connection as a computer-readable medium. Thus, any such connection is properly termed a computer-readable medium. Combinations of the above should also be included within the scope of the computer-readable media.
  • Computer-executable instructions include, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Computer-executable instructions also include program modules that are executed by computers in stand-alone or network environments. Generally, program modules include routines, programs, objects, components, and data structures, etc. that perform particular tasks or implement particular abstract data types. Computer-executable instructions, associated data structures, and program modules represent examples of the program code means for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represents examples of corresponding acts for implementing the functions described in such steps.
  • Those of skill in the art will appreciate that other embodiments of the invention may be practiced in network computing environments with many types of computer system configurations, including personal computers, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, and the like. Embodiments may also be practiced in distributed computing environments where tasks are performed by local and remote processing devices that are linked (either by hardwired links, wireless links, or by a combination thereof) through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.
  • Although the above description may contain specific details, they should not be construed as limiting the claims in any way. Other configurations of the described embodiments of the invention are part of the scope of this invention. For example, fraud tracking based on voiceprint could be applied to paypal transactions, myspace.com and other social networking websites, child protection services, or restricting convicted felons from purchasing firearms. Accordingly, the appended claims and their legal equivalents should only define the invention, rather than any specific examples given.

Claims (20)

We claim:
1. A method comprising:
verifying a user as authorized for a transaction by:
comparing a voice signal against a database of known fraudulent voice signatures, wherein the voice signal comprises an uncommonly spoken word;
comparing the voice signal against a database of voice signals associated with the transaction;
requesting an additional voice signal comprising personal information of the user when the voice signal does not substantially match an entry in the database of voice signals; and
comparing the personal information to a database of personal information; and
when the user cannot be verified as authentic, adding the voice signal to the database of known fraudulent voice signatures.
2. The method of claim 1, further comprising:
performing a transaction when the first voice signal substantially matches the speaker verification database and the personal information is verified.
3. The method of claim 1, further comprising:
denying further user action when the personal information is not verified.
4. The method of claim 1, further comprising:
employing a sliding threshold scale of confidence in query results obtained from the speaker verification database based on a confidence level of query results obtained from the database of known fraudulent voice signatures.
5. The method of claim 1, further comprising:
upon verifying the user as authentic, adding the voice signal to the database of voice signals.
6. The method of claim 1, wherein the personal information comprises a personal identification number.
7. The method of claim 1, wherein the personal information comprises one of a billing address and a card security code.
8. A system comprising:
a processor; and
a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising:
verifying a user as authorized for a transaction by:
comparing a voice signal against a database of known fraudulent voice signatures, wherein the voice signal comprises an uncommonly spoken word;
comparing the voice signal against a database of voice signals associated with the transaction;
requesting an additional voice signal comprising personal information of the user when the voice signal does not substantially match an entry in the database of voice signals; and
comparing the personal information to a database of personal information; and
when the user cannot be verified as authentic, adding the voice signal to the database of known fraudulent voice signatures.
9. The system of claim 8, the computer-readable storage medium having additional instructions stored which, when executed by the processor, result in operations comprising:
performing a transaction when the first voice signal substantially matches the speaker verification database and the personal information is verified.
10. The system of claim 8, the computer-readable storage medium having additional instructions stored which, when executed by the processor, result in operations comprising:
denying further user action when the personal information is not verified.
11. The system of claim 8, the computer-readable storage medium having additional instructions stored which, when executed by the processor, result in operations comprising:
employing a sliding threshold scale of confidence in query results obtained from the speaker verification database based on a confidence level of query results obtained from the database of known fraudulent voice signatures.
12. The system of claim 8, the computer-readable storage medium having additional instructions stored which, when executed by the processor, result in operations comprising:
upon verifying the user as authentic, adding the voice signal to the database of voice signals.
13. The system of claim 8, wherein the personal information comprises a personal identification number.
14. The system of claim 8, wherein the personal information comprises one of a billing address and a card security code.
15. A computer-readable storage device having instructions stored which, when executed by a computing device, cause the computing device to perform operations comprising:
verifying a user as authorized for a transaction by:
comparing a voice signal against a database of known fraudulent voice signatures, wherein the voice signal comprises an uncommonly spoken word;
comparing the voice signal against a database of voice signals associated with the transaction;
requesting an additional voice signal comprising personal information of the user when the voice signal does not substantially match an entry in the database of voice signals; and
comparing the personal information to a database of personal information; and
when the user cannot be verified as authentic, adding the voice signal to the database of known fraudulent voice signatures.
16. The computer-readable storage device of claim 15, having additional instructions stored which, when executed by the processor, result in operations comprising:
performing a transaction when the first voice signal substantially matches the speaker verification database and the personal information is verified.
17. The computer-readable storage device of claim 15, having additional instructions stored which, when executed by the processor, result in operations comprising:
denying further user action when the personal information is not verified.
18. The computer-readable storage device of claim 15, having additional instructions stored which, when executed by the processor, result in operations comprising:
employing a sliding threshold scale of confidence in query results obtained from the speaker verification database based on a confidence level of query results obtained from the database of known fraudulent voice signatures.
19. The computer-readable storage device of claim 15, having additional instructions stored which, when executed by the processor, result in operations comprising:
upon verifying the user as authentic, adding the voice signal to the database of voice signals.
20. The computer-readable storage device of claim 15, wherein the personal information comprises a personal identification number.
US14/481,348 2007-05-29 2014-09-09 System and Method for Tracking Fraudulent Electronic Transactions Using Voiceprints of Uncommon Words Abandoned US20150006397A1 (en)

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Families Citing this family (104)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8234494B1 (en) * 2005-12-21 2012-07-31 At&T Intellectual Property Ii, L.P. Speaker-verification digital signatures
US8359278B2 (en) 2006-10-25 2013-01-22 IndentityTruth, Inc. Identity protection
US8086461B2 (en) 2007-06-13 2011-12-27 At&T Intellectual Property Ii, L.P. System and method for tracking persons of interest via voiceprint
US10002189B2 (en) 2007-12-20 2018-06-19 Apple Inc. Method and apparatus for searching using an active ontology
US9330720B2 (en) 2008-01-03 2016-05-03 Apple Inc. Methods and apparatus for altering audio output signals
US8077836B2 (en) * 2008-07-30 2011-12-13 At&T Intellectual Property, I, L.P. Transparent voice registration and verification method and system
US20100030549A1 (en) 2008-07-31 2010-02-04 Lee Michael M Mobile device having human language translation capability with positional feedback
US8676904B2 (en) 2008-10-02 2014-03-18 Apple Inc. Electronic devices with voice command and contextual data processing capabilities
TWI412941B (en) * 2008-11-25 2013-10-21 Inst Information Industry Apparatus and method for generating and verifying a voice signature of a message and computer program product thereof
US20100328035A1 (en) * 2009-06-29 2010-12-30 International Business Machines Corporation Security with speaker verification
US8682667B2 (en) 2010-02-25 2014-03-25 Apple Inc. User profiling for selecting user specific voice input processing information
US9652802B1 (en) 2010-03-24 2017-05-16 Consumerinfo.Com, Inc. Indirect monitoring and reporting of a user's credit data
US9800721B2 (en) 2010-09-07 2017-10-24 Securus Technologies, Inc. Multi-party conversation analyzer and logger
WO2012112781A1 (en) 2011-02-18 2012-08-23 Csidentity Corporation System and methods for identifying compromised personally identifiable information on the internet
US9262612B2 (en) 2011-03-21 2016-02-16 Apple Inc. Device access using voice authentication
US10057736B2 (en) 2011-06-03 2018-08-21 Apple Inc. Active transport based notifications
US8819793B2 (en) 2011-09-20 2014-08-26 Csidentity Corporation Systems and methods for secure and efficient enrollment into a federation which utilizes a biometric repository
US11030562B1 (en) 2011-10-31 2021-06-08 Consumerinfo.Com, Inc. Pre-data breach monitoring
US8818810B2 (en) 2011-12-29 2014-08-26 Robert Bosch Gmbh Speaker verification in a health monitoring system
US10134385B2 (en) 2012-03-02 2018-11-20 Apple Inc. Systems and methods for name pronunciation
US8724779B2 (en) 2012-03-20 2014-05-13 International Business Machines Corporation Persisting customer identity validation during agent-to-agent transfers in call center transactions
US9721563B2 (en) 2012-06-08 2017-08-01 Apple Inc. Name recognition system
US9837078B2 (en) 2012-11-09 2017-12-05 Mattersight Corporation Methods and apparatus for identifying fraudulent callers
US8812387B1 (en) 2013-03-14 2014-08-19 Csidentity Corporation System and method for identifying related credit inquiries
US9620123B2 (en) * 2013-05-02 2017-04-11 Nice Ltd. Seamless authentication and enrollment
WO2014197335A1 (en) 2013-06-08 2014-12-11 Apple Inc. Interpreting and acting upon commands that involve sharing information with remote devices
EP3937002A1 (en) 2013-06-09 2022-01-12 Apple Inc. Device, method, and graphical user interface for enabling conversation persistence across two or more instances of a digital assistant
US10176167B2 (en) 2013-06-09 2019-01-08 Apple Inc. System and method for inferring user intent from speech inputs
US9646613B2 (en) 2013-11-29 2017-05-09 Daon Holdings Limited Methods and systems for splitting a digital signal
US10296160B2 (en) 2013-12-06 2019-05-21 Apple Inc. Method for extracting salient dialog usage from live data
US9472194B2 (en) * 2014-03-21 2016-10-18 Wells Fargo Bank, N.A. Enhanced fraud detection
US9633004B2 (en) 2014-05-30 2017-04-25 Apple Inc. Better resolution when referencing to concepts
US9430463B2 (en) 2014-05-30 2016-08-30 Apple Inc. Exemplar-based natural language processing
AU2015266863B2 (en) 2014-05-30 2018-03-15 Apple Inc. Multi-command single utterance input method
US10170123B2 (en) 2014-05-30 2019-01-01 Apple Inc. Intelligent assistant for home automation
US9818400B2 (en) 2014-09-11 2017-11-14 Apple Inc. Method and apparatus for discovering trending terms in speech requests
US10074360B2 (en) 2014-09-30 2018-09-11 Apple Inc. Providing an indication of the suitability of speech recognition
US9668121B2 (en) 2014-09-30 2017-05-30 Apple Inc. Social reminders
US10127911B2 (en) * 2014-09-30 2018-11-13 Apple Inc. Speaker identification and unsupervised speaker adaptation techniques
US10339527B1 (en) 2014-10-31 2019-07-02 Experian Information Solutions, Inc. System and architecture for electronic fraud detection
US9922048B1 (en) 2014-12-01 2018-03-20 Securus Technologies, Inc. Automated background check via facial recognition
US10152299B2 (en) 2015-03-06 2018-12-11 Apple Inc. Reducing response latency of intelligent automated assistants
US9886953B2 (en) 2015-03-08 2018-02-06 Apple Inc. Virtual assistant activation
US9721566B2 (en) 2015-03-08 2017-08-01 Apple Inc. Competing devices responding to voice triggers
US10567477B2 (en) 2015-03-08 2020-02-18 Apple Inc. Virtual assistant continuity
US10083688B2 (en) 2015-05-27 2018-09-25 Apple Inc. Device voice control for selecting a displayed affordance
US9578173B2 (en) 2015-06-05 2017-02-21 Apple Inc. Virtual assistant aided communication with 3rd party service in a communication session
US11025565B2 (en) 2015-06-07 2021-06-01 Apple Inc. Personalized prediction of responses for instant messaging
US11151468B1 (en) 2015-07-02 2021-10-19 Experian Information Solutions, Inc. Behavior analysis using distributed representations of event data
US9940934B2 (en) * 2015-11-18 2018-04-10 Uniphone Software Systems Adaptive voice authentication system and method
US10049668B2 (en) 2015-12-02 2018-08-14 Apple Inc. Applying neural network language models to weighted finite state transducers for automatic speech recognition
US9697836B1 (en) 2015-12-30 2017-07-04 Nice Ltd. Authentication of users of self service channels
CN107343077B (en) * 2016-04-28 2019-12-13 腾讯科技(深圳)有限公司 Method, device and equipment for identifying malicious phone and establishing identification model
US10049663B2 (en) 2016-06-08 2018-08-14 Apple, Inc. Intelligent automated assistant for media exploration
US10586535B2 (en) 2016-06-10 2020-03-10 Apple Inc. Intelligent digital assistant in a multi-tasking environment
DK179415B1 (en) 2016-06-11 2018-06-14 Apple Inc Intelligent device arbitration and control
DK201670540A1 (en) 2016-06-11 2018-01-08 Apple Inc Application integration with a digital assistant
US10474753B2 (en) 2016-09-07 2019-11-12 Apple Inc. Language identification using recurrent neural networks
US10043516B2 (en) 2016-09-23 2018-08-07 Apple Inc. Intelligent automated assistant
US11281993B2 (en) 2016-12-05 2022-03-22 Apple Inc. Model and ensemble compression for metric learning
US11204787B2 (en) 2017-01-09 2021-12-21 Apple Inc. Application integration with a digital assistant
US10277590B2 (en) * 2017-01-17 2019-04-30 International Business Machines Corporation Cognitive intelligence based voice authentication
US10417266B2 (en) 2017-05-09 2019-09-17 Apple Inc. Context-aware ranking of intelligent response suggestions
DK201770383A1 (en) 2017-05-09 2018-12-14 Apple Inc. User interface for correcting recognition errors
US10726832B2 (en) 2017-05-11 2020-07-28 Apple Inc. Maintaining privacy of personal information
DK201770439A1 (en) 2017-05-11 2018-12-13 Apple Inc. Offline personal assistant
US10395654B2 (en) 2017-05-11 2019-08-27 Apple Inc. Text normalization based on a data-driven learning network
DK179745B1 (en) 2017-05-12 2019-05-01 Apple Inc. SYNCHRONIZATION AND TASK DELEGATION OF A DIGITAL ASSISTANT
DK201770429A1 (en) 2017-05-12 2018-12-14 Apple Inc. Low-latency intelligent automated assistant
DK179496B1 (en) 2017-05-12 2019-01-15 Apple Inc. USER-SPECIFIC Acoustic Models
US11301477B2 (en) 2017-05-12 2022-04-12 Apple Inc. Feedback analysis of a digital assistant
DK201770432A1 (en) 2017-05-15 2018-12-21 Apple Inc. Hierarchical belief states for digital assistants
DK201770431A1 (en) 2017-05-15 2018-12-20 Apple Inc. Optimizing dialogue policy decisions for digital assistants using implicit feedback
US10311144B2 (en) 2017-05-16 2019-06-04 Apple Inc. Emoji word sense disambiguation
US10303715B2 (en) 2017-05-16 2019-05-28 Apple Inc. Intelligent automated assistant for media exploration
US10403278B2 (en) 2017-05-16 2019-09-03 Apple Inc. Methods and systems for phonetic matching in digital assistant services
DK179560B1 (en) 2017-05-16 2019-02-18 Apple Inc. Far-field extension for digital assistant services
US10657328B2 (en) 2017-06-02 2020-05-19 Apple Inc. Multi-task recurrent neural network architecture for efficient morphology handling in neural language modeling
US10592649B2 (en) 2017-08-09 2020-03-17 Nice Ltd. Authentication via a dynamic passphrase
US10445429B2 (en) 2017-09-21 2019-10-15 Apple Inc. Natural language understanding using vocabularies with compressed serialized tries
US10699028B1 (en) 2017-09-28 2020-06-30 Csidentity Corporation Identity security architecture systems and methods
US10755051B2 (en) 2017-09-29 2020-08-25 Apple Inc. Rule-based natural language processing
US10896472B1 (en) 2017-11-14 2021-01-19 Csidentity Corporation Security and identity verification system and architecture
US10630487B2 (en) * 2017-11-30 2020-04-21 Booz Allen Hamilton Inc. System and method for issuing a certificate to permit access to information
US10636424B2 (en) 2017-11-30 2020-04-28 Apple Inc. Multi-turn canned dialog
US10733982B2 (en) 2018-01-08 2020-08-04 Apple Inc. Multi-directional dialog
US10733375B2 (en) 2018-01-31 2020-08-04 Apple Inc. Knowledge-based framework for improving natural language understanding
US11275855B2 (en) 2018-02-01 2022-03-15 Nuance Communications, Inc. Conversation print system and method
US10789959B2 (en) 2018-03-02 2020-09-29 Apple Inc. Training speaker recognition models for digital assistants
US10592604B2 (en) 2018-03-12 2020-03-17 Apple Inc. Inverse text normalization for automatic speech recognition
US10818288B2 (en) 2018-03-26 2020-10-27 Apple Inc. Natural assistant interaction
US10909331B2 (en) 2018-03-30 2021-02-02 Apple Inc. Implicit identification of translation payload with neural machine translation
US10928918B2 (en) 2018-05-07 2021-02-23 Apple Inc. Raise to speak
US11145294B2 (en) 2018-05-07 2021-10-12 Apple Inc. Intelligent automated assistant for delivering content from user experiences
US10984780B2 (en) 2018-05-21 2021-04-20 Apple Inc. Global semantic word embeddings using bi-directional recurrent neural networks
US10892996B2 (en) 2018-06-01 2021-01-12 Apple Inc. Variable latency device coordination
DK201870355A1 (en) 2018-06-01 2019-12-16 Apple Inc. Virtual assistant operation in multi-device environments
DK180639B1 (en) 2018-06-01 2021-11-04 Apple Inc DISABILITY OF ATTENTION-ATTENTIVE VIRTUAL ASSISTANT
DK179822B1 (en) 2018-06-01 2019-07-12 Apple Inc. Voice interaction at a primary device to access call functionality of a companion device
US11386266B2 (en) 2018-06-01 2022-07-12 Apple Inc. Text correction
US10504518B1 (en) 2018-06-03 2019-12-10 Apple Inc. Accelerated task performance
US11176543B2 (en) * 2018-09-22 2021-11-16 Mastercard International Incorporated Voice currency token based electronic payment transactions
US10659588B1 (en) * 2019-03-21 2020-05-19 Capital One Services, Llc Methods and systems for automatic discovery of fraudulent calls using speaker recognition
US20220272124A1 (en) * 2021-02-19 2022-08-25 Intuit Inc. Using machine learning for detecting solicitation of personally identifiable information (pii)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020058506A1 (en) * 1996-02-05 2002-05-16 Amin Umesh J. Roaming authorization system
US7386105B2 (en) * 2005-05-27 2008-06-10 Nice Systems Ltd Method and apparatus for fraud detection
US7590538B2 (en) * 1999-08-31 2009-09-15 Accenture Llp Voice recognition system for navigating on the internet
US7721109B1 (en) * 2000-07-28 2010-05-18 Verizon Business Global Llc Secure transaction card using biometrical validation
US7822605B2 (en) * 2006-10-19 2010-10-26 Nice Systems Ltd. Method and apparatus for large population speaker identification in telephone interactions

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS58129682A (en) 1982-01-29 1983-08-02 Toshiba Corp Individual verifying device
US5339385A (en) * 1992-07-22 1994-08-16 Itt Corporation Speaker verifier using nearest-neighbor distance measure
US5414755A (en) * 1994-08-10 1995-05-09 Itt Corporation System and method for passive voice verification in a telephone network
JP3699608B2 (en) * 1999-04-01 2005-09-28 富士通株式会社 Speaker verification apparatus and method
US20060248019A1 (en) 2005-04-21 2006-11-02 Anthony Rajakumar Method and system to detect fraud using voice data
US9300790B2 (en) * 2005-06-24 2016-03-29 Securus Technologies, Inc. Multi-party conversation analyzer and logger

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020058506A1 (en) * 1996-02-05 2002-05-16 Amin Umesh J. Roaming authorization system
US7590538B2 (en) * 1999-08-31 2009-09-15 Accenture Llp Voice recognition system for navigating on the internet
US7721109B1 (en) * 2000-07-28 2010-05-18 Verizon Business Global Llc Secure transaction card using biometrical validation
US7386105B2 (en) * 2005-05-27 2008-06-10 Nice Systems Ltd Method and apparatus for fraud detection
US7822605B2 (en) * 2006-10-19 2010-10-26 Nice Systems Ltd. Method and apparatus for large population speaker identification in telephone interactions

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