Abstract
The goal of this paper is to review automatic systems for forensic speaker recognition (FSR) based on scientifically approved methods for calculation and interpretation of biometric evidence. The objective of this paper is not to promote one speaker recognition method against another, but is to make available to the biometric research community data-driven methodology combining automatic speaker recognition techniques and a rigorous forensic experimental background. Forensic speaker recognition is the process of determining if a specific individual (suspected speaker) is the source of a questioned speech recording (trace). This paper aims at reviewing forensic automatic speaker recognition (FASR) methods that provide a coherent way of quantifying and presenting recorded speech as biometric evidence, as well as the assessment of its strength (likelihood ratio) in the Bayesian interpretation framework compatible with interpretations in other forensic disciplines. Forensic speaker recognition has proven an effective tool in the fight against crime, yet there is a constant need for more research due to the difficulties involved because of the within-speaker (within-source) variability, between-speakers (between-sources) variability, and differences in recording sessions conditions.
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References
Aitken, C., Taroni, F.: Statistics and the Evaluation of Evidence for Forensic Scientists. John Wiley and Sons, Chichester (2004)
Alexander, A., Drygajlo, A.: Scoring and Direct Methods for the Interpretation of Evidence in Forensic Speaker Recognition. In: 8th International Conference on Spoken Language Processing (ICSLP 2004), Jeju, Korea, 2397–2400 (2004)
Association of Forensic Science Providers: Standards for the formulation of evaluative forensic science expert opinion. Science and Justice 49, 161–164 (2009)
Bolt, R.H., et al.: On the theory and practice of voice identification. National Academy of Sciences, Washington (1979)
Champod, C., Meuwly, D.: The Inference of Identity in Forensic Speaker Identification. Speech Communication 31(2–3), 193–203 (2000)
Drygajlo, A., Meuwly D., Alexander A.: Statistical Methods and Bayesian Interpretation of Evidence in Forensic Automatic Speaker Recognition. In: Proceedings of 8th European Conference on Speech Communication and Technology (Eurospeech 2003), Geneva, Switzerland, pp. 689–692 (2003)
Drygajlo, A.: Forensic Automatic Speaker Recognition. IEEE Signal Processing Magazine 24(2), 132–135 (2007)
Drygajlo, A.: Statistical Evaluation of Biometric Evidence in Forensic Automatic Speaker Recognition. In: Geradts, Z.J.M.H., Franke, K.Y., Veenman, C.J. (eds.) IWCF 2009. LNCS, vol. 5718, pp. 1–12. Springer, Heidelberg (2009)
Drygajlo A.: Forensic Evidence of Voice. In: Li, S.Z. (ed.) Encyclopedia of Biometrics, pp. 1388–1395. Springer, New York (2009)
Drygajlo A.: Automatic Speaker Recognition for Forensic Case Assessment and Interpretation. In: Neustein, A., Patil, H. (eds): Forensic Speaker Recognition: Law Enforcement and Counter-Terrorism, ch. 2., pp. 21–39. Springer, New York (2011)
Gonzalez-Rodriguez, J., Drygajlo, A., Ramos-Castro, D., Garcia-Gomar, M., Ortega-Garcia, J.: Robust estimation, interpretation and assessment of likelihood ratios in forensic speaker recognition. Computer Speech and Language 20(2–3), 331–355 (2006)
Jackman, S.: Bayesian Analysis for the Social Sciences. John Wiley and Sons, Chichester (2009)
Jackson, G., Jones, S., Booth, G., Champod, C., Evett, I.: The nature of forensic science opinion-a possible framework to guide thinking and practice in investigations and in court proceedings. Science and Justice 46, 33–44 (2006)
Jain, A. et al. (eds).: Handbook of Biometrics. Springer, New York (2008)
Jamieson, A., Moenssens, A. (eds.): Wiley Encyclopedia of Forensic Science. John Wiley and Sons, Chichester (2011)
Kinnunen T., Li H.: An overview of text-independent speaker recognition: From features to supervectors. Speech Communication 52, 12–40 (2010)
Meuwly, D., Drygajlo, A.: Forensic Speaker Recognition Based on a Bayesian Framework and Gaussian Mixture Modelling (GMM). A Speaker Odyssey, The Speaker Recognition Workshop, Crete, Greece, pp. 145–150 (2001)
Morrison, G.: Forensic Voice Comparison and the Paradigm Shift. Science and Justice 49, 298–308 (2009)
Nolan, F.: The Phonetic Bases of Speaker Recognition. Cambridge University Press, Cambridge (1983) (reissued 2009)
Ramos Castro, D.: Forensic Evaluation of the Evidence using Automatic Speaker Recognition Systems. Ph.D. thesis, Universidad Autonoma de Madrid, Madrid, Spain (2007)
Reynolds, D., Quatieri, T., Dunn, R.: Speaker verification using adapted Gaussian mixture models. Digital Signal Processing 10(1), 19–41 (2000)
Robertson, B., Vignaux, G.: Interpreting Evidence. Evaluating Forensic Science in the Courtroom. John Wiley and Sons, Chichester (1995)
Rose, P.: Forensic Speaker Identification. Taylor and Francis, London (2002)
Rose, P.: Technical forensic speaker recognition: Evaluation, types and testing of evidence. Computer Speech and Language 20(2–3), 159–191 (2006)
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Drygajlo, A. (2014). From Speaker Recognition to Forensic Speaker Recognition. In: Cantoni, V., Dimov, D., Tistarelli, M. (eds) Biometric Authentication. BIOMET 2014. Lecture Notes in Computer Science(), vol 8897. Springer, Cham. https://doi.org/10.1007/978-3-319-13386-7_8
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DOI: https://doi.org/10.1007/978-3-319-13386-7_8
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