Robust Session Variability Compensation for SVM Speaker Verification

Hyunson Seo, Chi Sang Jung, Hong Goo Kang

Research output: Contribution to journalArticle

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1631-1641
Number of pages11
JournalIEEE Transactions on Audio, Speech and Language Processing
Issue number6
Publication statusPublished - 2011 Aug

All Science Journal Classification (ASJC) codes

  • Acoustics and Ultrasonics
  • Electrical and Electronic Engineering

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