Feature selection for speaker verification using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Roisin Loughran and Alexandros Agapitos and 
                 Ahmed Kattan and Anthony Brabazon and Michael O'Neill",
  title =        "Feature selection for speaker verification using
                 genetic programming",
  journal =      "Evolutionary Intelligence",
  year =         "2017",
  volume =       "10",
  number =       "1-2",
  pages =        "1--21",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Speaker
                 verification, Feature selection, Unbalanced data",
  ISSN =         "1864-5917",
  DOI =          "doi:10.1007/s12065-016-0150-5",
  size =         "21 pages",
  abstract =     "We present a study examining feature selection from
                 high performing models evolved using genetic
                 programming (GP) on the problem of automatic speaker
                 verification (ASV). ASV is a highly unbalanced binary
                 classification problem in which a given speaker must be
                 verified against everyone else. We evolve
                 classification models for 10 individual speakers using
                 a variety of fitness functions and data sampling
                 techniques and examine the generalisation of each model
                 on a 1:9 unbalanced set. A significant difference
                 between train and test performance is found which may
                 indicate overfitting in the models. Using only the best
                 generalising models, we examine two methods for
                 selecting the most important features. We compare the
                 performance of a number of tuned machine learning
                 classifiers using the full 275 features and a reduced
                 set of 20 features from both feature selection methods.
                 Results show that using only the top 20 features found
                 in high performing GP programs led to test
                 classifications that are as good as, or better than,
                 those obtained using all data in the majority of
                 experiments undertaken. The classification accuracy
                 between speakers varies considerably across all
                 experiments showing that some speakers are easier to
                 classify than others. This indicates that in such
                 real-world classification problems, the content and
                 quality of the original data has a very high influence
                 on the quality of results obtainable.",

Genetic Programming entries for Roisin Loughran Alexandros Agapitos Ahmed Kattan Anthony Brabazon Michael O'Neill