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Which questions, propositions and ‘relevant populations’ should a speaker comparison expert assess?

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Rhodes, R., French, J. P., Harrison, P., Hughes, V., Kirchhubel, C. and Wormald, J. (2017) Which questions, propositions and ‘relevant populations’ should a speaker comparison expert assess? Paper presented at the International Association of Forensic Phonetics and Acoustics (IAFPA) conference, Split, Croatia. 9-12 July 2017.

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Which questions, propositions and ‘relevant populations’ should a speaker comparison expert assess?

  1. 1. Which questions, propositions and relevant populations should a speaker comparison expert assess? IAFPA 2017 Richard Rhodes, Peter French, Phil Harrison, Vince Hughes, Christin Kirchhübel & Jessica Wormald J P French λssociates
  2. 2. Themes ∇ How do we take speaker information & case information into account? ∇ How useful can you make evidence without overstepping expert role? ∇ How best to communicate the evidence & conditioning factors to decision-makers? ∇ What questions are we being asked? 2 J P French λssociates
  3. 3. BayesWatch Hp: Criminal speaker = suspect Hd: Criminal speaker = not the suspect/ someone other than suspect Hd = ideally provided by defence; agreed by all parties; specific enough to be tested 3 J P French λssociates
  4. 4. BayesWatch Hp: Criminal speaker = suspect Hd: Criminal speaker = someone other than suspect of similar age, gender, accent background Hd defines the population for typicality assessments + narrows down potential perps 4 J P French λssociates
  5. 5. In practice… ∇ Specific Hd provided in 1-2% of cases ∇ ‘Agreed’ information is rare, changeable ∇ Defence statements = week before trial ∇ Offering multiple Hds could = legal advice ∇ The impossible Hd ∇ The expert will rarely be present to explain: 5 J P French λssociates
  6. 6. Report in UK System
  7. 7. BayesWatch Expert almost always generates Hd Hp: Criminal speaker = suspect Hd: Criminal speaker = someone other than suspect of similar age, gender, accent background 8 J P French λssociates
  8. 8. Hicks et al No observations in propositions 9 J P French λssociates
  9. 9. Hicks et al London
  10. 10. Hicks et al London Male
  11. 11. Hicks et al London Male Adult (20-40)
  12. 12. Hicks et al London Male Adult (20-40) English L1
  13. 13. Hicks et al London Male Adult (20-40) Polish L1
  14. 14. Morrison et al – reply i Wide Hd makes analysis unreliable so Refine Hd with a screening to ensure a well- defined background population Sex/accent/age = not part of evidential value 15 J P French λssociates
  15. 15. Hicks et al - rejoinder 2-stage process: 1. Assess value of class-level characteristics 2. Assess value of features, given class 16 J P French λssociates
  16. 16. Morrison et al – reply ii Court can usually be expected to be able to determine language, sex, accent Does not require “scientific, technical, or other specialized knowledge” ∴ court can be left to evaluate class-level 17 J P French λssociates
  17. 17. Where does the expert’s job end? key difference = Hicks: expert helps user evaluate class-level; part of evidence value Morrison: expert explains class assumptions; but assumes user can evaluate value 18 J P French λssociates
  18. 18. Where does the expert’s job end? Defining properties of a voice part of expert’s role: ∇ Expert has the competence/skills ∇ Lay-people are bad at defining accents ∇ Users may not even listen to recordings ∇ Incorporation into evidence value depends on available information 19 J P French λssociates
  19. 19. Points to applaud ∇ Think about propositions for each case ∇ Class-level narrows down possible perps. ∇ Background population should match well ∇ Evidence should be compatible with reasonable prior expectations of users ∇ Assumptions should be made clear 20 J P French λssociates
  20. 20. Hierarchy of propositions 21 J P French λssociates
  21. 21. Hierarchy of propositions Level Generic Example propositions III Offence Hp Mr X committed the robbery; Hd Mr X did not commit the robbery. II Activity Hp Mr X is the man who demanded goods/money in the shop; Hd The offender was another man (with a similar demographic profile) who had access/opportunity to rob the shop. I Source Hp The offender speech on the CCTV came from Mr X; Hd The offender speech on the CCTV came from another person with a similar demographic profile. 22 J P French λssociates
  22. 22. Hierarchy of propositions Level Generic Example propositions III Offence Hp Mr X committed the robbery; Hd Mr X did not commit the robbery. II Activity Hp Mr X is the man who demanded goods/money in the shop; Hd The offender was another man (with a similar demographic profile) who had access/opportunity to rob the shop. I Source Hp The offender speech on the CCTV came from Mr X; Hd The offender speech on the CCTV came from another person with a similar demographic profile. 0 Neutral (Hicks et al) Hp The offender speech on the CCTV came from Mr X; Hd The offender speech on the CCTV came from another person 23 J P French λssociates
  23. 23. Case examples Welsh village; robbery CCTV; London accent Hp : criminal robber = suspect Defence Hd (Offence level):‘Not guilty’ plea Assumed sourcelevel Hd : criminal robber = another LondonEngspeaker Assumed activitylevel Hd : criminal robber = another LondonEngspeaker in rural welsh village 24 J P French λssociates
  24. 24. Case example: source/activity Manchester area; robbery; Aussie ♀ speaker Hp : criminal = suspect Defence Hd (Offence level):‘Not guilty’ plea 2nd Defence Hd : imaginary person ‘Sheila’ Assumed sourcelevel Hd : another Aussie Assumed activitylevel Hd(s): another Aussie in UK > NW England > Greater Manc > area? 25 J P French λssociates
  25. 25. Case example: Hicks/Morrison Manchester area; robbery; Aussie speaker Hp : criminal = suspect Morrison Hd: another young adult female speaker with Australian accent Hicks Hd 1: another person (in GM area) Hicks Hd 2: another young adult female speaker with Australian accent Combine LRs for 1 and 2 26 J P French λssociates
  26. 26. Thoughts ∇ Cases are unique ∇ Reports have many audiences ∇ Certainty and clarity of assumptions is key ∇ Voices are not manufactured like trainers ∇ Test and train for class-level judgements ∇ Is there a distinction between class features and individual features? 27 J P French λssociates
  27. 27. Themes ∇ How useful can you make the evidence without usurping role of decision-makers? Higher up the hierarchy = more useful; but relies on more outside information Where information is available = help users with class-level evaluations? 28 J P French λssociates
  28. 28. Themes ∇ How do we take speaker information and case information into account? Experts should know the range of possible propositions, and how to select types for each case 29 J P French λssociates
  29. 29. Themes ∇ What question are we being asked? Is the default Hd giving the right answer to the question being asked? 30 J P French λssociates
  30. 30. Themes ∇ How best to communicate the evidence & conditioning factors to decision-makers? Clarity and transparency to avoid double- counting or under-estimation 31 J P French λssociates
  31. 31. Thanks for listening and thanks to Vince Hughes (the phantom co-author) IAFPA 2017 Richard Rhodes, Peter French, Phil Harrison, Vince Hughes, Christin Kirchhübel & Jessica Wormald J P French λssociates
  32. 32. IAFPA 2017 Richard Rhodes, Peter French, Phil Harrison, Vince Hughes, Christin Kirchhübel & Jessica Wormald J P French λssociates • [UK] Association of Forensic Science Providers. (2009). Standards for the formulation of evaluative forensic science expert opinion. Science & Justice, 49, 161-164. • Cook, R., Evett, I. W., Jackson, G., Jones, P. J., & Lambert, J. A. (1998). A hierarchy of propositions: deciding which level to address in casework. Science & Justice, 38(4), 231-239. • Hicks, T., Biedermann, A., de Koeijer, J. A., Taroni, F., Champod, C., & Evett, I. W. (2015). The importance of distinguishing information from evidence/observations when formulating propositions. Science & Justice, 55(6), 520-525. • Hicks, T, et al [as above] (2017). Reply to Morrison et al.(2016) [below] Science & Justice. • Morrison, G. S., Enzinger, E., & Zhang, C. (2016). Refining the relevant population in forensic voice comparison–A response to Hicks et alii (2015) [above]. Science & Justice, 56(6), 492- 497. • Morrison et al [as above] (2017). Reply to Hicks et al reply [above] – online @ http://geoff- morrison.net/

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