In some situations the quality of the signals involved in a speaker verification trial is not as good as needed to take a reliable decision. In this work, we present a new method based on Bayesian networks and quality measures to estimate if the trial decision is reliable. We present experiments on the NIST SRE2010 dataset degraded with additive noise. A system well calibrated for clean speech, produces a large actual DCF on the degraded dataset. We use our method to discard the unreliable trials and achieve a dramatic improvement of the cost values. We also prove that our method outperforms previously published approaches.
Bibliographic reference. Villalba, Jesús / Lleida, Eduardo / Ortega, Alfonso / Miguel, Antonio (2013): "A new Bayesian network to assess the reliability of speaker verification decisions", In INTERSPEECH-2013, 3132-3136.