Data-Driven Gaussian Component Selection for Fast GMM-Based Speaker Verification

@inproceedings{Zhang2011DataDrivenGC,
  title={Data-Driven Gaussian Component Selection for Fast GMM-Based Speaker Verification},
  author={Ce Zhang and Rong Zheng and Bo Xu},
  booktitle={INTERSPEECH},
  year={2011}
}
In this paper, a fast likelihood calculation of Gaussian mixture model (GMM) is presented, by means of dividing the acoustic space into disjoint subsets and then assigning the most relevant Gaussians to each of them. The data-driven approach is explored to select Gaussian component which guarantees that the loss, brought by pre-discarding most useless Gaussians, can be easily controlled by a manual set parameter. To avoid the rapid growth of the index table size, a two level index scheme is… CONTINUE READING

Citations

Publications citing this paper.
Showing 1-2 of 2 extracted citations

Cluster-based senone selection for the efficient calculation of deep neural network acoustic models

2016 10th International Symposium on Chinese Spoken Language Processing (ISCSLP) • 2016
View 4 Excerpts
Highly Influenced

Data-driven tree structure based UBM reconstruction for speaker verification

The 9th International Symposium on Chinese Spoken Language Processing • 2014
View 2 Excerpts

Similar Papers

Loading similar papers…