0:00:15 | time |
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0:00:15 | so we first moved with allocation problem gmm-ubm systems |
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0:00:20 | the motivation is the biometric characteristics are not secret in the h increasing surveillance so |
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0:00:27 | think about you have a financial institution use all your one data there |
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0:00:31 | not the financial situation that was we line whose line and biometrics need to be |
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0:00:38 | projected on this protection it's be strong and from |
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0:00:41 | also to financial institutions got take that shouldn't be a lot that from one |
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0:00:45 | the data from one you cannot apply to the other issue transfer there |
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0:00:49 | just as an example and that's why we want to real research but what's biometric |
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0:00:55 | template protection for speaker recognition |
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0:00:58 | where you can go for biometric crypto system or can similar biometrics |
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0:01:04 | we wanted to |
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0:01:06 | emphasized on the properties of the ice was extended to four seven four five |
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0:01:11 | which is about biometric template protection and the basic things on there are very simple |
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0:01:15 | energy so i got the biometric one the data and i cannot which we've |
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0:01:20 | any thing right because it's to do much to not |
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0:01:24 | and unlike ability so i five and one system i cannot retrieve any information one |
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0:01:29 | of the system although they are based on the same ubm for example |
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0:01:33 | and what we propose a small bit allocation |
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0:01:38 | you can use what follows for the commitment or for divorce or schemes also for |
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0:01:43 | fast searches and basic idea is to extract common mfcc features and that supervector and |
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0:01:51 | then combined voice the supervectors |
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0:01:53 | and four button to template protection scheme using for example haven't distance comparison |
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0:02:01 | for improving the performance we also we did expected the most discriminant features so for |
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0:02:08 | that we use the relevant projection thank can see that post |
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