ISCA Archive Odyssey 2010
ISCA Archive Odyssey 2010

Cosine Similarity Scoring without Score Normalization Techniques

Najim Dehak, Reda Dehak, James Glass, Douglas Reynolds, Patrick Kenny

In recent work [1], a simplified and highly effective approach to speaker recognition based on the cosine similarity between low-dimensional vectors, termed ivectors, defined in a total variability space was introduced. The total variability space representation is motivated by the popular Joint Factor Analysis (JFA) approach, but does not require the complication of estimating separate speaker and channel spaces and has been shown to be less dependent on score normalization procedures, such as z-norm and t-norm. In this paper, we introduce a modification to the cosine similarity that does not require explicit score normalization, relying instead on simple mean and covariance statistics from a collection of impostor speaker ivectors. By avoiding the complication of z- and t-norm, the new approach further allows for application of a new unsupervised speaker adaptation technique to models defined in the ivector space. Experiments are conducted on the core condition of the NIST 2008 corpora, where, with adaptation, the new approach produces an equal error rate (EER) of 4.8% and min decision cost function (MinDCF) of 2.3% on all female speaker trials.

Cite as: Dehak, N., Dehak, R., Glass, J., Reynolds, D., Kenny, P. (2010) Cosine Similarity Scoring without Score Normalization Techniques. Proc. The Speaker and Language Recognition Workshop (Odyssey 2010), paper 15

  author={Najim Dehak and Reda Dehak and James Glass and Douglas Reynolds and Patrick Kenny},
  title={{Cosine Similarity Scoring without Score Normalization Techniques}},
  booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2010)},
  pages={paper 15}