@article{332,
  author = {Mohamed Soltane, Noureddine Doghmane, Nourddine Guersi},
  title = {State of the Art: Speech Biometrics Verification},
  journal = {Journal of Information Technology Review},
  year = {2010},
  volume = {1},
  number = {3},
  doi = {},
  url = {http://www.dline.info/jitr/fulltext/v1n3/3.pdf},
  abstract = {Gaussian mixture models (GMMs) remain the state of the art technique for modeling spectral envelope features for speech recognition systems. This paper presents a comparative analysis of the performance of three estimation algorithms Expectation Maximization (EM), Greedy EM Algorithm (GEM) and Figueiredo-Jain Algorithm (FJ) based Gaussian mixture models (GMMs) for text-independent speech biometrics verification. The simulation results are showed significant performance achievements. The test performance of, EER=0.26 % for “EM”, EER=0.21 % for “GEM” and EER=0.16 % for “FJ”, show that the behavioral information scheme of speech biometrics is more robust and have a discriminating power, which can be explored for identity authentication.},
}