Analysis of i-vector Length Normalization in Speaker Recognition Systems

  title={Analysis of i-vector Length Normalization in Speaker Recognition Systems},
  author={Daniel Garcia-Romero and Carol Y. Espy-Wilson},
We present a method to boost the performance of probabilistic generative models that work with i-vector representations. The proposed approach deals with the nonGaussian behavior of i-vectors by performing a simple length normalization. This non-linear transformation allows the use of probabilistic models with Gaussian assumptions that yield equivalent performance to that of more complicated systems based on Heavy-Tailed assumptions. Significant performance improvements are demonstrated on the… CONTINUE READING
Highly Influential
This paper has highly influenced 85 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 837 citations. REVIEW CITATIONS
560 Citations
8 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 560 extracted citations

837 Citations

Citations per Year
Semantic Scholar estimates that this publication has 837 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-8 of 8 references

Support Vector Machines versus Fast Scoring in the Low-Dimensional Total Variability Space for Speaker Verification

  • N. Dehak
  • Interspeech 2009, Brighton, UK, 2009.
  • 2009
2 Excerpts

Similar Papers

Loading similar papers…