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High-Level Speaker Verification via Articulatory-Feature based Sequence Kernels and SVM

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Issue:

Technology

 

Written by:

Rick D

 

Date added:

March 10, 2015

 

Level:

 

Grade:

A

 

No of pages / words:

11 / 2848

 

Was viewed:

6204 times

 

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Essay content:

Therefore, the method does not fully utilize the discriminative information available in the training data. To fully harness the discriminative information, this paper proposes training a support vector machine (SVM) for computing the verification scores. More precisely, the models of target speakers, individual background speakers, and claimants are converted to AF-supervectors, which form the inputs to an AF-based kernel of the SVM for computing verification scores...
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More precisely, the models of target speakers, individual background speakers, and claimants are converted to AF-supervectors, which form the inputs to an AF-based kernel of the SVM for computing verification scores. Results show that the proposed AF-kernel scoring is complementary to likelihood-ratio scoring, leading to better performance when the two scoring methods are combined...
displayed 300 characters

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