A Bayesian network approach combining pitch and spectral envelope features to reduce channel mismatch in speaker verification and forensic speaker recognition

Abstract

The aim of this paper is to reduce the effect of mismatch in recording conditions due to the transmission channel and recording device, using conditional dependencies of prosodic and spectral envelope features. The developed system is based on a Bayesian network framework which combines statistical models of the pitch and spectral envelope features. This approach is applied to forensic automatic speaker recognition, where mismatched recording conditions pose a serious problem to the accurate estimation of the strength of voice evidence. The method is evaluated using a forensic speaker recognition database that contains three different recording conditions typical to forensic tasks. The performance of the system is evaluated using both speaker verification as well as forensic speaker recognition measures.