Abstract
Issues of development of modern approaches to probabilistic assessment of forensic research results have been receiving increased amounts of attention owing to the need for clear characteristics of the limitations of research results, which include indicators of uncertainty of obtained data and associated estimated probabilities. In the modern theory of judicial evidence assessment, the use of probabilities is acceptable and even preferred, and one of the main provisions is the principle of comparing probabilities in light of their dependency on competitive versions that arise from the adversarial nature of court proceedings. In this regard, the purpose of this article is to develop methodological approaches to the use of the likelihood ratio as the most appropriate way of determining the significance of conclusions submitted to the court by forensic experts for the formation of evidentiary foundation. A brief overview of publications from 2000–2018 concerned with forensic applications of the concept of the likelihood ratio is given. This concept can be used to reliably assess the credibility of evidence. In this article the term “evidence” is considered as various continuous quantitative measurements (of properties and features of forensic objects) that are used to compare a known sample with a questioned one and establish whether they originate from the same source or from different sources. The article discusses the most common normal distribution of continuous data and a general approach to calculating the likelihood ratio (LR) using probability density functions (pdfs). It is shown that accounting for the variability of the compared samples when calculating LR requires three databases: a potential database, a control database of the known sample, and a comparative database of the questioned sample. Examples of calculating LR and the strength of evidence for various types of examinations are given. The procedures for calculating LR are generally similar, but the authors suggest different techniques of calculating and graphically representing the strength of evidence. The value of the so-called cost of (penalty for) incorrect results (ClLR), is considered in detail; the concepts of its validity and reliability, as well as its credibility interval, are introduced. The article highlights a number of specific features of calculating LR for multivariate continuous data. Of great interest is the forensic audio analysis application of speaker models in the form of weighted sums of Gaussian densities of M components (Gaussian mixture models, GMM), where each component is a D-dimensional Gaussian pdf with an average vector value and a covariance matrix. It can be assumed that the use of GMM pdfs for calculating LR is effective not only for forensic audio examination but also for other types of examinations. The universal applicability of using the likelihood ratio to assess the similarity/difference of forensic objects indicates high viability of the approach.
Similar content being viewed by others
Notes
In mathematics, a vector is a list of more than one variable. The index p refers to the number of observed parameters, for example, several ratios of concentrations of elements in the composition of the glass (x1, …, xp); the transposition of a column vector into a row vector is denoted as T.
REFERENCES
Evett, J.W., Towards a uniform framework for reporting opinions in forensic science case-work, Sci. Justice, 1998, vol. 38, no. 3, pp. 198–202. https://doi.org/10.1016/S1355-0306(98)72105-7
Champod, C. and Meuwly, D., The inference of identity in forensic speaker recognition, Speech Commun., 2000, vol. 31, pp. 193–203. https://doi.org/10.1016/S0167-6393(99)00078-3
Koons, R.D. and Buscaglia, J., Interpretation of glass composition measurements: the effects of match criteria on discrimination capability, J. Forensic Sci., 2001, vol. 47, no. 3, pp. 505–512. https://doi.org/10.1520/JFS2001349
Aitken, C.G.G. and Lucy, D., Evaluation of trace evidence in the form of multivariate data, Appl. Stat., 2004, vol. 53, no. 1, pp. 109–122. https://doi.org/10.26896/1028-6861-2018-84-6-70-76
Aitken, C.G.G. and Taroni, F., Statistics and the Evaluation of Forensic Evidence for Forensic Scientist, Chichester: Wiley, 2004.
Botti, F., Alexander, A., and Drygajlo, A., On compensation of mismatched recording conditions in the Bayesian approach for forensic automatic speaker recognition, Forensic Sci. Int., 2004, vol. 146, suppl. 2, pp. S101–S106. https://doi.org/10.1016/j.forsciint.2004.09.032
Balding, D.J., Weight-of-Evidence for Forensic DNA Profiles, Chichester: Wiley, 2005.
Curran, M., An introduction to Bayesian credible intervals for sampling error in DNA profiles, Law, Probab. Risk, 2005, vol. 4, pp. 115–126. https://doi.org/10.1093/lpr/mgi009
González-Rodríguez, J., Drygajlo, A.V., Ramos-Castro, D., et al., Robust estimation, interpretation and assessment of likelihood ratios in forensic speaker recognition, Comput. Speech Lang., 2006, vol. 20, pp. 331–355. https://doi.org/10.1016/j.csl.2005.08.005
Rose, P., Technical forensic speaker recognition: evaluation, types and testing of evidence, Comput. Speech Lang., 2006, vol. 20, nos. 2–3, pp. 159–191. https://doi.org/10.1016/j.csl.2005.07.003
Morrison, G.S., Forensic voice comparison and the paradigm shift, Sci. Justice, 2009, vol. 49, pp. 298–308. https://doi.org/10.1016/j.scijus.2009.09.002
Association of Forensic Science Providers, Standards for the formulation of evaluative forensic science expert opinion, Sci. Justice, 2009, vol. 49, pp. 161–164. https://doi.org/10.1016/j.scijus.2009.07.004
Rose, P. and Morrison, G.S., A response to the UK position statement on forensic speaker comparison, Int. J. Speech Lang. Law, 2009, vol. 16, pp. 139–163. https://doi.org/10.1558/ijsll.v16i1.139
Taroni, F., Bozza, S., Biedermann, A., et al., Data Analysis in Forensic Science: A Bayesian Decision Perspective, New York: Wiley, 2010.
Zadora, G. and Ramos, D., Evaluation of glass samples for forensic purposes—An application of likelihood ratios and an information–theoretical approach, Chemom. Intell. Lab. Syst., 2010, vol. 102, pp. 63–68. https://doi.org/10.1016/j.chemolab.2010.007
Morrison, G., Measuring the validity and reliability of forensic likelihood-ratio systems, Sci. Justice, 2011, vol. 51, no. 3, pp. 91–98. https://doi.org/10.1016/j.scijus.2011.03.002
Neumann, C., Evett, J.W., and Skerrett, J., Quantifying the weight of evidence from a forensic fingerprint comparison: a new paradigm, J. R. Stat. Soc., Ser. A, 2012, vol. 175, no. 2, pp. 371–415. https://doi.org/10.1111/j.1467-985X.2011.01027.x
Bebeshko, G.I., Omelyuk, G.G., and Usov, A.I., The role and significance of likelihood ratio concept in assessment and interpretation of the results of forensic activities, Zavod. Lab., Diagn. Mater., 2018, vol. 84, no. 6, pp. 70–76. https://doi.org/10.26896/1028-6861-2018-84-6-70-76
Gradusova, O.V. and Kuz’min, S.A., Probability interpretation of forensic evidence, Teor. Prakt. Sud. Ekspert., 2017, vol. 12, no. 4, pp. 27–40.
Nefedov S.N., Bayesian approach to evidence assessment and standardization of verbal statements of expert conclusions, in Problemy ukrepleniya zakonnosti i pravoporyadka: nauka, praktika, tendentsii (Problems of strengthening the rules of law and Order: science, practice, trends), Minsk: Resp. Inst. Prof. Obraz., 2015, no. 8, pp. 187–195.
Lindley, D.V., A problem in forensic science, Biometrika, 1977, vol. 64, pp. 207–213.
Curran, J.M., Triggs, C.M., Almirall, J.R., et al., The interpretation of elemental composition from forensic glass evidence: I, Sci. Justice, 1997, vol. 37, no. 4, pp. 241–244. https://doi.org/10.1016/S1355-0306(97)72197-X
Curran, J.M., Triggs, C.M., Almirall, J.R., et al., The interpretation of elemental composition from forensic glass evidence: II, Sci. Justice, 1997, vol. 37, no. 4, pp. 245–249. https://doi.org/10.1016/S1355-0306(97)72198-1
Koons, R.D. and Buscaglia, J., Interpretation of glass composition measurements: the effects of match criteria on discrimination capability, J. Forensic Sci., 2002, vol. 47, pp. 505–512.
Martyna, A., Lucy, D., Zadora, G.V., et al., The evidential value of microspectrophotometry measurements made for pen inks, Anal. Methods, 2013, vol. 5, pp. 6788–6795. https://doi.org/10.1039/C341622D
Brümmer, N. and du Preez, J., Application independent evaluation of speaker detection, Comput. Speech Lang., 2006, vol. 20, nos. 2–3, pp. 230–275. https://doi.org/10.1016/j.csl.2005.08.001
Ramos, D. and Gonzalez-Rodriguez, J., Reliable support: measuring calibration of likelihood ratios, Forensic Sci. Int., 2013, vol. 230, nos. 1–3, pp. 156–169. https://doi.org/10.1016/j.forsciint.2013.04.014
Ramos, D., Gonzalez-Rodriguez, J., Zadora, G., and Aitken, C., Information-theoretical assessment of the performance of likelihood ratio computation methods, J. Forensic Sci., 2013, vol. 58, no. 6, pp. 1503–1518. https://doi.org/10.1111/1556-4029.12233
Drygajlo, A., Automatic speaker recognition for forensic case assessment and interpretation, in Forensic Speaker Recognition: Law Enforcement and Counter-Terrorism, Neustein, A. and Patil, H.A., Eds., New York: Springer-Verlag, 2012, ch. 2, pp. 21–39. https://doi.org/10.1007/978-1-4614-0263-3_2
Kinnunen, T. and Li, H., An overview of text-independent speaker recognition: from features to supervectors, Speech Commun., 2010, vol. 52, no. 1, pp. 12–40. https://doi.org/10.1016/j.specom.2009.08.009
Meuwly, D. and Drygajlo, A., Forensic speaker recognition based on a Bayesian framework and Gaussian mixture modeling (GMM), Proc. 2001 Speaker Recognition Workshop A Speaker Odyssey, Crete, Greece, June 18–22, 2001, Los Angeles: ISCA, 2001.
Reynolds, D.A., Automatic speaker recognition using Gaussian mixture speaker models, Lincoln Lab. J., 1995, vol. 8, no. 2, pp. 173–191.
Reynolds, D.A., Quatieri, T.F., and Dunn, R.B., Speaker verification using adapted Gaussian mixture models, Digital Signal Process., 2000, vol. 10, nos. 1–3, pp. 19–41. https://doi.org/10.1006/dspr.1999.0361
Matveev, Yu.N., Technologies for biometric identification of individuals by voice and other modalities, Vestn. Mosk. Gos. Tekh. Univ. im. N.E. Baumana, 2012, no. 3 (3), pp. 46–61.
Kozlov, A.V., Kudashev, O.Yu., Matveev, Yu.N., et al., Speaker recognition system for the NIST SRE 2012, Inf. Avtom., 2013, no. 2 (25), pp. 350–370.
Morrison, G.S., Zhang, C., and Rose, P., An empirical estimate of the precision of likelihood ratios from a forensic-voice-comparison system, Forensic Sci. Int., 2011, vol. 208, pp. 59–65. https://doi.org/10.1016/j.forsciint.2010.11.001
González-Rodríguez, J., Rose, P., Ramos, D., et al., Emulating DNA: rigorous quantification of evidential weight in transparent and testable forensic speaker recognition, IEEE Trans. Audio Speech Lang. Process., 2007, vol. 15, pp. 2104–2115. https://doi.org/10.1109/TASL.2007.902747
Morrison, G.S., Likelihood-ratio forensic voice comparison using parametric representations of the formant trajectories of diphthongs, J. Acoust. Soc. Am., 2009, vol. 125, pp. 2387–2397. https://doi.org/10.1121/1.3081384
van Leeuwen, D.A. and Brümmer, N., An introduction to application-independent evaluation of speaker recognition systems, in Speaker Classification I: Fundamentals, Features, and Methods, Müller, C., Ed., Berlin: Springer-Verlag, 2007, pp. 330–353. https://doi.org/10.1007/978-3-540-74200-5_19
Morrison, G.S., Forensic voice comparison using likelihood ratios based on polynomial curves fitted to the formant trajectories of Australian English/ai/, Int. J. Speech Lang. Law, 2008, vol. 15, pp. 247–264. https://doi.org/10.1558/ijsll.v15i2.249
Morrison, G.S., A comparison of procedures for the calculation of forensic likelihood ratios from acoustic-phonetic data: multivariate kernel density (MVKD) versus Gaussian mixture model–universal background model (GMM-UBM), Speech Commun., 2011, vol. 53, pp. 242–256. https://doi.org/10.1016/j.specom.2010.09.005
Aitken, C.G.G., Statistical discriminant analysis in forensic science, J. Forensic Sci. Soc., 1986, vol. 26, pp. 237–247.
Berry, D.A., Evett, I.W., and Pinchin, R., Statistical inference in crime investigations using deoxyribonucleic acid profiling (with discussion), Appl. Stat., 1992, vol. 41, pp. 499–531. https://doi.org/10.1111/j.1467-9876.1992/tb02418.x
Chan, K.P.S. and Aitken, C.G.G., Estimation of the Bayes’ factor in a forensic science problem, J. Stat. Comput. Simul., 1989, vol. 33, pp. 249–264.
Brümmer, N., Burget, L., Cernocký, J.H., et al., Fusion of heterogenous speaker recognition systems in the STBU submission for the NIST SRE 2006, IEEE Trans. Audio Speech Lang. Process., 2007, vol. 15, pp. 2072–2084. https://doi.org/10.1109/TASL.2007.902870
Pigeon, S., Druyts, P., and Verlinde, P., Applying logistic regression to the fusion of the NIST’99 1-speaker submissions, Digital Signal Process., 2000, vol. 10, pp. 237–248. https://doi.org/10.1006/dspr.1999.0358
Martyna, A., Michalska, A., and Zadora, G., Interpretation of FTIR spectra of polymers and Raman spectra of car paints by means of likelihood ratio approach supported by wavelet transform for reducing data dimensionality, Anal. Bioanal. Chem., 2015, vol. 407, pp. 3357–3376. https://doi.org/10.1007/s00216-015-8558-9
Daubechies, I., Ten Lectures on Wavelets (CBMS-NSF Regional Conference Series in Applied Mathematics), Philadelphia: Soc. Ind. Appl. Math., 1992.
Author information
Authors and Affiliations
Corresponding authors
Additional information
Translated by A. Ovchinnikova
Rights and permissions
About this article
Cite this article
Smirnova, S.A., Bebeshko, G.I., Omel’yanyuk, G.G. et al. Developing Evidentiary Foundation Based on Assessment of Forensic Results. Inorg Mater 57, 1431–1439 (2021). https://doi.org/10.1134/S0020168521140107
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S0020168521140107