Buy custom High-Level Speaker Verification via Articulatory-Feature based Sequence Kernels and SVM
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| Topic: |
Technology
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| Number of pages / Number of words: |
11 / 2848 |
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Second, the goal of speaker verification is to
minimize classification errors on test utterances rather than on individual
speech frames. These drawbacks motivate us to derive a sequence-based approach
in which an utterance is considered comprising a sequence of symbols and the
utterance-based score can be obtained from a support vector machine (SVM)
through a kernel function of the sequence of symbols...
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For each target speaker, the observation
sequences (AF labels) derived from his/her utterances are used to train a
phonetic-class dependent articulatory feature-based pronunciation model
(CD-AFCPM) \cite{Zhang&Mak&Meng07}. These models are then converted to
fixed-dimension AF supervectors for training a speaker-dependent SVM to
discriminate the target speaker from background speakers in the AF-supervector
space...
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