Statistical models in forensic voice comparison


This chapter describes a number of signal-processing and statistical-modeling techniques that are commonly used to calculate likelihood ratios in human-supervised automatic approaches to forensic voice comparison. Techniques described include mel frequency cepstral coefficients (MFCCs) feature extraction, Gaussian mixture model - universal background model (GMM-UBM) systems, i-vector - probabilistic linear discriminant analysis (i-vector PLDA) systems, deep neural network (DNN) based systems (including senone posterior i-vectors, bottleneck features, and embeddings / x-vectors), mismatch compensation, and score to likelihood ratio conversion (aka calibration). Empirical validation of forensic voice comparison systems is also covered. The aim of the chapter is to bridge the gap between general introductions to forensic voice comparison and the highly technical automatic speaker recognition literature from which the signal-processing and statistical-modeling techniques are mostly drawn. Knowledge of the likelihood ratio framework for the evaluation of forensic evidence is assumed. It is hoped that the material presented here will be of value to students of forensic voice comparison and to researchers interested in learning about statistical modeling techniques that could potentially also be applied to data from other branches of forensic science.

Divisions: College of Business and Social Sciences > Aston Institute for Forensic Linguistics
College of Business and Social Sciences > School of Social Sciences & Humanities
Additional Information: This is an Accepted Manuscript of a book chapter published by CRC Press in Handbook of Forensic Statistics on 28 Sept 2020, available online:
ISBN: 9781138295407
Last Modified: 23 Feb 2024 08:19
Date Deposited: 07 Mar 2019 11:29
Full Text Link: http://handbook ...
Related URLs: https://www.crc ... k/9781138295407 (Publisher URL)
PURE Output Type: Chapter (peer-reviewed)
Published Date: 2020-09-28
Accepted Date: 2019-02-23
Authors: Morrison, Geoffrey Stewart (ORCID Profile 0000-0001-8608-8207)
Enzinger, Ewald
Ramos, Daniel
González-Rodríguez, Joaquín
Lozano-Díez, Alicia



Version: Accepted Version

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