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Merge pull request #20 from w3c/voice-recognition
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Alternatives to Visual Turing Tests on the We

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EN 301 549, section 5.3a> [<cite><a class="bibref" data-link-type="biblio" href="#bib-en-301-549" title="EN 301 549 v3.2.1: Harmonised European Standard - Accessibility requirements for ICT products and services">en-301-549a>cite>] and regulations under section 508 of
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the Rehabilitation Act and <a href="https://www.law.cornell.edu/cfr/text/36/appendix-C_to_part_1194">Section
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255 of the Communications Act, 36 CFR 1194, Appendix Ca>, section 403 in the
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United States [<cite><a class="bibref" data-link-type="biblio" href="#bib-36-cfr-1194" title="36 CFR Appendix C to Part 1194, Functional Performance Criteria and Technical Requirements">36-cfr-1194a>cite>].p>
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United States [<cite><a class="bibref" data-link-type="biblio" href="#bib-36-cfr-1194" title="36 CFR Appendix C to Part 1194, Functional Performance Criteria and Technical Requirements">36-cfr-1194a>cite>]. As a further example, the use of voice recognition (i.e., speaker identification) as a biometric identifier can be problematic for those with speech or hearing-related disabilities.p>
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<p>For this reason, biometric identification systems should be designed to allow users to choose among multiple and unrelated biometric identifiers. With that sole caveat, properly designed biometric identification systems are particularly attractive in situations where it is necessary to identify a particular human user. Their reliability is high, the cognitive load placed on the user low, and they are particularly difficult to foil. However, conventional applications of biometric authentication verify, and therefore disclose, the user's identity. They are thus unsuitable under circumstances in which it is desirable to preserve the user's anonymity reasons of privacy, while nevertheless establishing that the entity attempting to access an online service is human. The scheme described in the next section is designed to solve this problem.p>
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