Evaluation of MSR Identity Toolbox under conditions reflecting those of a real forensic case (forensic_eval_01)

This paper presents an evaluation of the Microsoft Research Identity Toolbox version 1.0 developed at Microsoft Research, as a tool for forensic voice comparison under conditions reflecting those of a real forensic case. For this purpose we implement two systems: the first is based on Gaussian mixture model - universal background model (GMM-UBM) and the second on i-vectors with probabilistic linear discriminant analysis (i-vector PLDA). Three different feature-level mismatch compensation techniques were tested, before and after the application of voice activity detection (VAD). The three techniques were global cepstral mean subtraction (CMS), global cepstral mean and variance normalization (CMVN), and local feature warping (FW). (C) 2017 Elsevier B.V. All rights reserved.