svm_training

Copyright 2014-2015 Anthony Larcher

svm_training provides utilities to train Support Vector Machines to perform speaker verification.

svm_training.svm_training(svmDir, background_sv, enroll_sv, numThread=1)[source]

Train Suport Vector Machine classifiers for two classes task (as implemented for nowbut miht change in the future to include multi-class classification) Training is parallelized on multiple threads.

Parameters:
  • svmDir – directory where to store the SVM models
  • background_sv – StatServer of super-vectors for background impostors. All super-vectors are used without selection
  • enroll_sv – StatServer of super-vectors used for the target models
  • numThread – number of thread to launch in parallel
svm_training.svm_training_singleThread(K, msn, bsn, svmDir, background_sv, models, enroll_sv)[source]

Train Suport Vector Machine classifiers for two classes task (as implemented for nowbut miht change in the future to include multi-class classification)

Parameters:
  • K – pre-computed part of the Gram matrix
  • msn – maximum number of sessions to train a SVM
  • bsn – number of session used as background impostors
  • svmDir – directory where to store the SVM models
  • background_sv – StatServer of super-vectors for background impostors. All super-vectors are used without selection
  • models – list of models to train. The models must be included in the enroll_sv StatServer
  • enroll_sv – StatServer of super-vectors used for the target models

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