Maximum Likelihood Acoustic Factor Analysis Models for Robust Speaker Verification in Noise

@article{Hasan2014MaximumLA,
  title={Maximum Likelihood Acoustic Factor Analysis Models for Robust Speaker Verification in Noise},
  author={Taufiq Hasan and John H. L. Hansen},
  journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
  year={2014},
  volume={22},
  pages={381-391}
}
Recent speaker recognition/verification systems generally utilize an utterance dependent fixed dimensional vector as features to Bayesian classifiers. These vectors, known as i-Vectors, are lower dimensional representations of Gaussian Mixture Model (GMM) mean super-vectors adapted from a Universal Background Model (UBM) using speech utterance features, and extracted utilizing a Factor Analysis (FA) framework. This method is based on the assumption that the speaker dependent information resides… CONTINUE READING