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The impact in forensic voice comparison of lack of calibration and of mismatched conditions between the known-speaker recording and the relevant-population sample recordings

Morrison, Geoffrey Stewart·2018·Forensic Science International
DOI 10.1016/j.forsciint.2017.12.024
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Research works printed but not linked: 21

  1. Morrison, 2017, Assessing the admissibility of a new generation of forensic voice comparison testimony, Columbia Sci. Technol. Law Rev., 18, 326
  2. Morrison, 2018, Admissibility of forensic voice comparison testimony in England and Wales, Crim. Law Rev., 20
  3. Morrison, 2014, Distinguishing between forensic science and forensic pseudoscience: testing of validity and reliability, and approaches to forensic voice comparison, Sci. Just., 54, 245, 10.1016/j.scijus.2013.07.004 · DOI
  4. Lander, 2017, Response to the ANZFSS council statement on the President’s Council of Advisors on science and technology report, Aust. J. Forensic. Sci., 49, 366, 10.1080/00450618.2017.1304992 · DOI
  5. Reynolds, 2000, Speaker verification using adapted Gaussian mixture models, Digital Signal Process., 10, 19, 10.1006/dspr.1999.0361 · DOI
  6. S.O. Sadjadi, M. Slaney, L. Heck, MSR Identity Toolbox v1.0: A MATLAB Toolbox for speaker recognition research, IEEE Speech Lang. Process. Tech. Committee Newsletter, November 2013, http://archive.signalprocessingsociety.org/technical-committees/list/‌sl-tc/spl-nl/2013-11/IdentityToolbox/.
  7. da Silva, 2017, Evaluation of MSR Identity Toolbox under conditions reflecting those of a real forensic case (forensic_eval_01), Speech Commun., 94, 42, 10.1016/j.specom.2017.09.001 · DOI
  8. Davis, 1980, Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences, IEEE Trans. Acoust. Speech Signal Process., 28, 357, 10.1109/TASSP.1980.1163420 · DOI · Paper page
  9. Furui, 1986, Speaker-independent isolated word recognition using dynamic features of speech spectrum, IEEE Trans. Acoust. Speech Signal Process., 34, 52, 10.1109/TASSP.1986.1164788 · DOI
  10. Furui, 1981, Cepstral analysis technique for automatic speaker verification, IEEE Trans. Acoust. Speech Signal Process., 29, 254, 10.1109/TASSP.1981.1163530 · DOI
  11. Viikki, 1998, Cepstral domain segmental feature vector normalization for noise robust speech recognition, Speech Commun., 25, 133, 10.1016/S0167-6393(98)00033-8 · DOI
  12. Burnham, 2011, Building an audio-visual corpus of Australian English: large corpus collection with an economical portable and replicable black box, Proc. Interspeech, 841, 10.21437/Interspeech.2011-309 · DOI
  13. Enzinger, 2016, A demonstration of the application of the new paradigm for the evaluation of forensic evidence under conditions reflecting those of a real forensic-voice-comparison case, Sci. Just., 56, 42, 10.1016/j.scijus.2015.06.005 · DOI
  14. Pigeon, 2000, Applying logistic regression to the fusion of the NIST’99 1-speaker submissions, Digital Signal Process., 10, 237, 10.1006/dspr.1999.0358 · DOI
  15. González-Rodríguez, 2007, Emulating DNA: rigorous quantification of evidential weight in transparent and testable forensic speaker recognition, IEEE Trans. Audio Speech Lang. Process., 15, 2104, 10.1109/TASL.2007.902747 · DOI
  16. Morrison, 2013, Tutorial on logistic-regression calibration and fusion: converting a score to a likelihood ratio, Aust. J. Forensic Sci., 45, 173, 10.1080/00450618.2012.733025 · DOI
  17. Morrison, 2016, Multi-laboratory evaluation of forensic voice comparison systems under conditions reflecting those of a real forensic case (forensic_eval_01)—introduction, Speech Commun., 85, 119, 10.1016/j.specom.2016.07.006 · DOI
  18. Morrison, 2012, Protocol for the collection of databases of recordings for forensic-voice-comparison research and practice, Aust. J. Forensic Sci., 44, 155, 10.1080/00450618.2011.630412 · DOI
  19. Brümmer, 2006, Application independent evaluation of speaker detection, Comput. Speech Lang., 20, 230, 10.1016/j.csl.2005.08.001 · DOI
  20. Morrison, 2011, Measuring the validity and reliability of forensic likelihood-ratio systems, Sci. Just., 51, 91, 10.1016/j.scijus.2011.03.002 · DOI
  21. Meuwly, 2016, A guideline for the validation of likelihood ratio methods used for forensic evidence evaluation, Forensic Sci. Int.

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Metadata

Authors
Morrison, G. S.
ORCID
Morrison, Geoffrey Stewart
Venue
Forensic Science International
Publisher
Elsevier BV
Published
2018-02
Volume
283
Pages
e1-e7
ISSN
0379-0738
DOI
10.1016/j.forsciint.2017.12.024
Cited by
18
References
0
Language
en
Licence

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