Likelihood ratio calibration in a transparent and testable forensic speaker recognition framework

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. D. Ramos, J. González-Rodríguez, J. Ortega-garcía, "Likelihood Ratio Calibration in a Transparent and Testable Forensic Speaker Recognition Framework " in The Speaker and Language Recognition Workshop, ODYSSEY, San Juan (Puerto Rico), 2006, 1 - 8A recently reopened debate about the infallibility of some classical forensic disciplines is leading to new requirements in forensic science. Standardization of procedures, proficiency testing, transparency in the scientific evaluation of the evidence and testability of the system and protocols are emphasized in order to guarantee the scientific objectivity of the procedures. Those ideas will be exploited in this paper in order to walk towards an appropriate framework for the use of forensic speaker recognition in courts. Evidence is interpreted using the Bayesian approach for the analysis of the evidence, as a scientific and logical methodology, in a two-stage approach based in the similarity-typicality pair, which facilitates the transparency in the process. The concept of calibration as a way of reporting reliable and accurate opinions is also deeply addressed, presenting experimental results which illustrate its effects. The testability of the system is then accomplished by the use of the NIST SRE 2005 evaluation protocol. Recently proposed application-independent evaluation techniques (Cllr and APE curves) are finally addressed as a proper way for presenting results of proficiency testing in courts, as these evaluation metrics clearly show the influence of calibration errors in the accuracy of the inferential decision processThis work has been supported by the Spanish Ministry for Science and Technology under project TIC2003-09068-C02-01

An investigation of supervector regression for forensic voice comparison on small data

International audienceThe present paper deals with an observer design for a nonlinear lateral vehicle model. The nonlinear model is represented by an exact Takagi-Sugeno (TS) model via the sector nonlinearity transformation. A proportional multiple integral observer (PMIO) based on the TS model is designed to estimate simultaneously the state vector and the unknown input (road curvature). The convergence conditions of the estimation error are expressed under LMI formulation using the Lyapunov theory which guaranties bounded error. Simulations are carried out and experimental results are provided to illustrate the proposed observer

Empirical test of the performance of an acoustic-phonetic approach to forensic voice comparison under conditions similar to those of a real case

In a 2012 case in New South Wales, Australia, the identity of a speaker on several audio recordings was in question. Forensic voice comparison testimony was presented based on an auditory-acoustic-phonetic-spectrographic analysis. No empirical demonstration of the validity and reliability of the analytical methodology was presented. Unlike the admissibility standards in some other jurisdictions (e.g., US Federal Rule of Evidence 702 and the Daubert criteria, or England & Wales Criminal Practice Directions 19A), Australia's Unified Evidence Acts do not require demonstration of the validity and reliability of analytical methods and their implementation before testimony based upon them is presented in court. The present paper reports on empirical tests of the performance of an acoustic-phonetic-statistical forensic voice comparison system which exploited the same features as were the focus of the auditory-acoustic-phonetic-spectrographic analysis in the case, i.e., second-formant (F2) trajectories in /o/ tokens and mean fundamental frequency (f0). The tests were conducted under conditions similar to those in the case. The performance of the acoustic-phonetic-statistical system was very poor compared to that of an automatic system. © 2017 Elsevier B.V

Text-dependent Forensic Voice Comparison: Likelihood Ratio Estimation with the Hidden Markov Model (HMM) and Gaussian Mixture Model – Universal Background Model (GMMUBM) Approaches

Among the more typical forensic voice comparison (FVC) approaches, the acoustic-phonetic statistical approach is suitable for text-dependent FVC, but it does not fully exploit available time-varying information of speech in its modelling. The automatic approach, on the other hand, essentially deals with text-independent cases, which means temporal information is not explicitly incorporated in the modelling. Text-dependent likelihood ratio (LR)-based FVC studies, in particular those that adopt the automatic approach, are few. This preliminary LR-based FVC study compares two statistical models, the Hidden Markov Model (HMM) and the Gaussian Mixture Model (GMM), for the calculation of forensic LRs using the same speech data. FVC experiments were carried out using different lengths of Japanese short words under a forensically realistic, but challenging condition: only two speech tokens for model training and LR estimation. Log-likelihood-ratio cost (Cllr) was used as the assessment metric. The study demonstrates that the HMM system constantly outperforms the GMM system in terms of average Cllr values. However, words longer than three mora are needed if the advantage of the HMM is to become evident. With a seven-mora word, for example, the HMM outperformed the GMM by a Cllr value of 0.073

Measuring coherence of computer-assisted likelihood ratio methods

This is the author’s version of a work that was accepted for publication in Forensic Science International. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Forensic Science International, 249 (2015): 123 – 132 DOI:10.1016/j.forsciint.2015.01.033Measuring the performance of forensic evaluation methods that compute likelihood ratios (LRs) is relevant for both the development and the validation of such methods. A framework of performance characteristics categorized as primary and secondary is introduced in this study to help achieve such development and validation. Ground-truth labelled fingerprint data is used to assess the performance of an example likelihood ratio method in terms of those performance characteristics. Discrimination, calibration, and especially the coherence of this LR method are assessed as a function of the quantity and quality of the trace fingerprint data. Assessment of the coherence revealed a weakness of the comparison algorithm in the computer-assisted likelihood ratio method used.This research was conducted in the scope of the BBfor2 – European Commission Marie Curie Initial Training Network (FP7-PEOPLE-ITN-2008 under Grant Agreement 238803) at the Netherlands Forensic Institute, and in collaboration with the ATVS Biometric Recognition Group at the Universidad Autonoma de Madrid and the National Police Services Agency of the Netherlands

Performance of likelihood ratios considering bounds on the probability of observing misleading evidence

This is a pre-copyedited, author-produced version of an article accepted for publication in Law, Probability & Risk following peer review. The version of record Jose Juan Lucena-Molina, Daniel Ramos-Castro, Joaquin Gonzalez-Rodriguez; Performance of likelihood ratios considering bounds on the probability of observing misleading evidence. Law, Probability and Risk 2015; 14 (3): 175-192 is available online at: http://dx.doi.org/10.1093/lpr/mgu022In this article, we introduce a new tool, namely 'Limit Tippett Plots', to assess the performance of likelihood ratios in evidence evaluation including theoretical bounds on the probability of observing misleading evidence. To do that, we first review previous work about such bounds. Then we derive 'Limit Tippett Plots' that complements Tippett plots with information about the limits on the probability of observing misleading evidence, which are taken as a reference. Thus, a much richer way to measure performance of likelihood ratios is given. Finally, we present an experimental example in forensic automatic speaker recognition following the protocols of the Acoustics Laboratory of Guardia Civil, where it can be seen that 'Limit Tippett Plots' help to detect problems in the calculation of likelihood ratios

Gaussian Mixture Models of Between-Source Variation for Likelihood Ratio Computation from Multivariate Data

Franco-Pedroso J, Ramos D, Gonzalez-Rodriguez J (2016) Gaussian Mixture Models of Between-Source Variation for Likelihood Ratio Computation from Multivariate Data. PLoS ONE 11(2): e0149958. doi:10.1371/journal.pone.0149958In forensic science, trace evidence found at a crime scene and on suspect has to be evaluated from the measurements performed on them, usually in the form of multivariate data (for example, several chemical compound or physical characteristics). In order to assess the strength of that evidence, the likelihood ratio framework is being increasingly adopted. Several methods have been derived in order to obtain likelihood ratios directly from univariate or multivariate data by modelling both the variation appearing between observations (or features) coming from the same source (within-source variation) and that appearing between observations coming from different sources (between-source variation). In the widely used multivariate kernel likelihood-ratio, the within-source distribution is assumed to be normally distributed and constant among different sources and the between-source variation is modelled through a kernel density function (KDF). In order to better fit the observed distribution of the between-source variation, this paper presents a different approach in which a Gaussian mixture model (GMM) is used instead of a KDF. As it will be shown, this approach provides better-calibrated likelihood ratios as measured by the log-likelihood ratio cost (C-llr) in experiments performed on freely available forensic datasets involving different trace evidences: inks, glass fragments and car paints.JFP recieved funding from "Ministerio de Economia y Competitividad (ES)" (http://www.mineco.gob.es/) through the project "CMC-V2: Caracterizacion, Modelado y Compensacion de Variabilidad en la Senal de Voz", with grant number TEC2012-37585-C02-01. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript