Quality measures based calibration with duration and noise dependency for speaker recognition

Research output: Contribution to journalArticle


  • Miranti Indar Mandasari
  • Rahim Saeidi
  • David A. Van Leeuwen

Research units

  • Radboud University Nijmegen


Abstract This paper studies the effect of short utterances and noise on the performance of automatic speaker recognition. We focus on calibration aspects, and propose a calibration strategy that uses quality measures to model the calibration parameters. We carry out the proposed calibration by using simple Quality Measure Functions (QMFs) of duration and measured signal-to-noise-ratio from speech segments. We test the effectiveness of the approach using two databases, the development set of the I4U collaboration for the NIST Speaker Recognition Evaluation (SRE) 2012, and the evaluation test material of NIST SRE 2012 itself. In comparison with conventional linear calibration, results show that the proposed QMF approach successfully improves the system performance in terms of both discrimination and calibration.


Original languageEnglish
Article number2297
Pages (from-to)126-137
Number of pages12
JournalSpeech Communication
Publication statusPublished - 14 Jun 2015
MoE publication typeA1 Journal article-refereed

    Research areas

  • Calibration, Duration, Noise, Quality measure function, Speaker recognition

ID: 10273411