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VOICE BASED SECURITY SYSTEM

VBSS

VBSS SECURITY BEYOND PASSWORD

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                Security System are defined as "automated methods of identifying or authenticating the identity of a living person based on a physical or behavioral characteristic." Unique physical traits, such as fingerprints, iris scans, voice prints, faces, signatures or the geometry of the hand can be used. Voice can combine what people say with the way that they say it, which means that it provides two-factor authentication in a single action. Fingerprints, iris scans, retina scans, and face recognition can all produce biometric identification (what you are), but something else is needed to provide a second and more secure authentication factor (something you know, for instance). Not only can voice combine two factors, but it can also do it more efficiently. Voice Based Security Systems uses a person's voice print to uniquely identify individuals using biometric speaker verification technology. Speech is processed through a non-contact method; you do not need to see or to touch the person to be able to recognize them."Biometric technologies - those that use..voice.. - will be the most important IT innovations of the next several years. -Bill Gates at Gartner Group Itexpo.  

                Today password is going to be the major security factor which is prone to hackers. The password is not sufficient enough today to secure critical data. The security system based on the voice will analyze the pitch and word and compares it with the data inside the system and recognizes the user and shows his own data. Personal information like the banking passwords, Bank amount details, Intelligence Data, Criminal data, Case Files of the Lawyers, Critical Defense information etc, can be stored based on the Voice data.

              In this project we present two voice-to-phoneme conversion algorithms that extract voice-tag abstractions for speaker independent voice-tag applications in embedded platforms, which are very sensitive to memory and CPU consumptions. In the first approach, a voice-to-phoneme conversion in batch mode manages this task by preserving the commonality of input feature vectors of multiple voice-tag example utterances. Given multiple example utterances, a developed feature combination strategy produces an "average utterance, which is converted to phonetic strings as a voice-tag representation via a speaker-independent phonetic decoder. In the second approach, a sequential voice-to-phoneme conversion algorithm uncovers the hierarchy of phonetic consensus embedded among multiple phonetic hypotheses generated by a speaker-independent phonetic decoder from multiple example utterances of a voice-tag. The most relevant phonetic hypotheses are then chosen to represent the voice-tag.  


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