ISCA Archive Interspeech 2013
ISCA Archive Interspeech 2013

Minimax i-vector extractor for short duration speaker verification

Ville Hautamäki, You-Chi Cheng, Padmanabhan Rajan, Chin-Hui Lee

Total variability modeling, based on i-vector extraction of converting a variable-length sequence of feature vectors into a fixed-length i-vector, is currently an adopted parametrization technique for state of-the-art speaker verification systems. However, when the number of the feature vectors is low, uncertainty in the i-vector representation as a point estimate of the linear-Gaussian model is understandably problematic. It is known that the zeroth and first order sufficient statistics, given the hyperparameters, completely characterize the extracted i-vectors. In this study we propose to use a minimax strategy to estimate the sufficient statistics in order to increase the robustness of the extracted i-vectors. We show by experiments that the proposed minimax technique can improve over the baseline system from 9.89% to 7.99% on the NIST SRE 2010 8conv-10sec task.

doi: 10.21437/Interspeech.2013-696

Cite as: Hautamäki, V., Cheng, Y.-C., Rajan, P., Lee, C.-H. (2013) Minimax i-vector extractor for short duration speaker verification. Proc. Interspeech 2013, 3708-3712, doi: 10.21437/Interspeech.2013-696

  author={Ville Hautamäki and You-Chi Cheng and Padmanabhan Rajan and Chin-Hui Lee},
  title={{Minimax i-vector extractor for short duration speaker verification}},
  booktitle={Proc. Interspeech 2013},