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Voice Biometric Overview for SfTelephony Meetup March 10, 2011 Dan Miller Opus Research.

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Presentation on theme: "Voice Biometric Overview for SfTelephony Meetup March 10, 2011 Dan Miller Opus Research."— Presentation transcript:

1 Voice Biometric Overview for SfTelephony Meetup March 10, 2011 Dan Miller Opus Research

2 © 2011 Opus Research, Inc. Why I’m here Talk about voice biometrics Share some ideas on stronger authentication for mobile transactions Get your feedback as prospective users/developers/implementers Describe some “real world” use cases, business cases and demand drivers 2

3 © 2011 Opus Research, Inc. Page 4 Voice Biometrics and Speaker Verification Voice Biometrics is a technology  Captures an utterance from a live caller  Compares it to previously stored “voiceprint”  Produces a score Speaker Verification is an application  Employs a biometric engine plus business logic  Enrolls customers by obtaining voice prints  Compares live utterances to voice prints to produce a “pass” or “fail” responses

4 © 2011 Opus Research, Inc. Page 4 Speaker Verification Components Core Verification Engine  Receives voice sample (“utterance”); compares it to a voiceprint (“template”)  Confirms who said it Core Recognition Engine  Compares utterance to ASR grammar  Determines what was said Business Logic  Decides if the caller passes or fails  Dictates required “next steps”

5 © 2011 Opus Research, Inc. Page 5 What is a Voice Print? Physical Characteristics The unique physical traits of the individual’s vocal tract, such as shape and size. Behavioral Characteristics The harmonic and resonant frequencies, such as accents, the speed of your speech, and how words are pronounced and emphasized. Voiceprint - Together these physiological and behavioral factors combine to produce unique voice patterns for every individual

6 © 2011 Opus Research, Inc. Page 6 Verification vs. Identification For Verification:  User claims an ID  Application matches voiceprint to that claim For Identification:  No claim of identity  ID System tries to detect “closest match” of captured utterances to voiceprint from a population of registered users

7 © 2011 Opus Research, Inc. Page 7 Text Dependent vs. Text Independent Applications that require a specific pass phrase are Text Dependent  Require training  Customarily involve enrollment Text Independent applications can use any utterance  Simplify enrollment  Support “conversational authentication”

8 © 2011 Opus Research, Inc. Why Now? 8

9 © 2011 Opus Research, Inc. Fraud protection persistence 9 Multifactor  Mandated in more use cases  Includes “something you are” Multimodal  Because “the customer is always on”  Embraces social networks and multiple sign-ons Mobile  Approaching 6 billion subscribers  Mobile devices are becoming virtual assistants

10 © 2011 Opus Research, Inc. +1 = Momentum Passwords getting more difficult  Multiple digits and special characters  Frequently updated  Fragmented across sites (and IDs) Authentication becoming important  To access multiple sites, domains and devices  For more activities, transactions and interactions  “Open” approaches only as strong as weakest link 10

11 © 2011 Opus Research, Inc. Application strengths Mobile payment authorization Device activation Access control Password reset Anonymous authentication 11

12 © 2011 Opus Research, Inc. Perspectives from RSA 12

13 The “Phone Channel” Traditionally Has Weaker Security ANI detection Voice profile (gender, age etc.) based on intuition Phone number Address Weak Identity verification Mother’s maiden name Social Security Number Basic account knowledge (last purchase etc.)

14 Fraudster call center online order form (with English translation) Fraudster call center online order form (with English translation) “Professional callers”: fluent in numerous languages, both male and female Caller-ID spoofing Service availability during American and Western European business hours. Cost: $7-$15 per phone call, Complete fraudulent transactions by impersonating people across a broad spectrum of demographics i.e. 77-year old female fluent in English or a middle-aged man fluent in Italian. Fraudster-Operated Call Centers Emerge in the Underground Economy to Facilitate Phone Fraud

15 Fraudster Operated Call Centers Underground forum post advertising "Professional Call Service"

16 Fraudster Operated Call Centers Review of a fraudster call center service

17 * Available H1 2008 How Multi-Channel Fraud is Perpetrated

18 Tools of the trade: VOIP (IPBX) ID Spoofing Delivery: War dialing SMS Email Already in play in the US Vishing

19 How Fraudsters Bypass Blacklisted Call Center Numbers Fraudster calls Spoofing access point Directs call to non-blacklisted phone number with Spoofed Caller ID Call Forwarding Device Call is forwarded to call center 800 number Call Center services unsuspicious inbound call displaying spoofed ID of an existing customer

20 Fraudsters’ Interest in Phone Banking

21 © 2011 Opus Research, Inc. And Speaker V & I can help Questions? Contact: dmiller@opusresearch.netdmiller@opusresearch.net Or on Twitter @dnm54 Page 21


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