Speaker Verification on Unbalanced Data with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{EvoIasp16Loughranetal,
  author =       "Roisin Loughran and Alexandros Agapitos and 
                 Ahmed Kattan and Anthony Brabazon and Michael O'Neill",
  title =        "Speaker Verification on Unbalanced Data with Genetic
                 Programming",
  booktitle =    "19th European Conference on the Applications of
                 Evolutionary Computation",
  year =         "2016",
  editor =       "Giovanni Squillero and Paolo Burelli",
  series =       "Lecture Notes in Computer Science",
  volume =       "9597",
  pages =        "737--753",
  address =      "Porto, Portugal",
  month =        mar # " 30 - " # apr # " 1",
  organisation = "EvoStar",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Speaker
                 verification, Unbalanced data, Feature selection",
  URL =          "http://dx.doi.org/10.1007/978-3-319-31204-0_47",
  DOI =          "doi:10.1007/978-3-319-31204-0_47",
  abstract =     "Automatic Speaker Verification (ASV) is a highly
                 unbalanced binary classification problem, in which any
                 given speaker must be verified against everyone else.
                 We apply Genetic programming (GP) to this problem with
                 the aim of both prediction and inference. We examine
                 the generalisation of evolved programs using a variety
                 of fitness functions and data sampling techniques found
                 in the literature. A significant difference between
                 train and test performance, which can indicate
                 overfitting, is found in the evolutionary runs of all
                 to-be-verified speakers. Nevertheless, in all speakers,
                 the best test performance attained is always superior
                 than just merely predicting the majority class. We
                 examine which features are used in good-generalising
                 individuals. The findings can inform future
                 applications of GP or other machine learning techniques
                 to ASV about the suitability of feature-extraction
                 techniques.",
  notes =        "EvoApplications2016 held in conjunction with
                 EuroGP'2016, EvoCOP2016 and EvoMusArt2016",
}

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

Citations