This paper presents an enhanced nuisance attribute projection (NAP) method to improve the performance of speaker verification systems in mismatched train and test conditions. Unlike the conventional NAP training method that does not take any scheme to discriminate the source of nuisance, the proposed method quantitatively estimates the source of nuisance based on the statistics of given background speakers' eigenvalues. The estimated values are used for defining a discriminative weight for each of background speakers and selectively including the statistics of between-class scatter or of within-class scatter from them. Through the scheme, we intend to design a more robust projection matrix which involves less speaker-dependent or speaker-intrinsic variability while including more latent nuisance factors beyond the common within-class scatter of backgrounds. Experimental results on the recent NIST SRE evaluations demonstrate that the proposed algorithms produce consistent improvement over the previous NAP approaches.
|Number of pages||11|
|Journal||IEEE Transactions on Audio, Speech and Language Processing|
|Publication status||Published - 2011 Aug|
Bibliographical noteFunding Information:
Manuscript received February 02, 2010; revised July 12, 2010; accepted November 01, 2010. Date of publication November 18, 2010; date of current version June 01, 2011. This work was supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science, and Technology (2010-0013394). The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Brian Mak.
All Science Journal Classification (ASJC) codes
- Acoustics and Ultrasonics
- Electrical and Electronic Engineering