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Defining the relevant population according to class and age when computing numerical likelihood ratios for forensic voice comparison
- 2. 2 1.0 Introduction • forensic voice comparison (FVC) = voice of offender (unknown) vs. voice of suspect (known) is the person on the criminal recording the same as the person on the suspect recording? • auditory-acoustic linguistic-phonetic analysis – analysis of a range of segmental (vowels, consonants), suprasegmental (f0, intonation, AR), higher-order linguistic (lexical choice, syntax) and VQ/vocal setting Hughes & Foulkes BAAP 2014
- 3. 1.0 Introduction 3 • the expert cannot say how likely it is that the suspect and offender are the same speaker (SS): – this is the job of the trier of fact (judge/ jury) – requires access to all of the evidence in the case • the expert can assess the strength (or weight) of a piece of the evidence under the competing hypotheses of the prosecution (same-speaker, SS) and defence (different-speaker, DS) p(Hss|E) ✗ Hughes & Foulkes BAAP 2014
- 4. Hughes & Foulkes BAAP 2014 1.0 Introduction 4 • the expert cannot say how likely it is that the suspect and offender are the same speaker (SS): – this is the job of the trier of fact (judge/ jury) – requires access to all of the evidence in the case • the expert can assess the strength (or weight) of a piece of the evidence under the competing hypotheses of the prosecution (same-speaker, SS) and defence (different-speaker, DS) p(Hss|E) ✗ p(E|Hss) p(E|Hds)LR = ✓
- 5. 1.0 Introduction 5 • likelihood ratio (LR) involves assessment of both similarity and typicality – it matters “whether the values found matching … are vanishingly rare, or sporadic, or near universal” (Nolan 2001:16) • typicality = defined by patterns in the relevant population (Aitken & Taroni 2004) – quantified relative to a sample of the population – outcome = value centered on 1 Hughes & Foulkes BAAP 2014
- 6. 1.0 Introduction Hughes & Foulkes BAAP 2014 6 • defence hypothesis (Hd): “it wasn’t the suspect, (it was … someone else)” • common approach = ‘logical relevance’ – between-S variation: language (region)/ sex – within-S variation: non-contemporaneity (Rose 2004) But: many other sources of systematic between-S and within-S variation identified in (socio)linguistics
- 7. 1.0 Introduction 7 – paradox: without knowing who the offender is, we can’t know (for certain) the population of which he is a member Between-speaker variation Within-speaker variation Regional background Phonological environment Sex (and gender) Topic Ethnicity Interlocutor Social networks Health Age Emotion Social class Non-contemporaneity
- 8. 1.0 Introduction 8 Research questions (i) to what extent are LRs affected by different definitions of the relevant population, in respect of social class and age? (i) how do these sources of between-S variation interact with each other to affect LR output? Hughes & Foulkes BAAP 2014
- 9. 2.1 Data: /eɪ/ (FACE) 9 • ONZE corpus (Gordon et al 2007) – NZ English, from Christchurch NZ – all males (for this study) – spontaneous speech, sociolinguistic interviews • auto-generated F1, F2, F3 trajectories – time-normalisedmeasurements at +10% steps (McDougall 2004, 2006) – procedures implemented for formant correction (incl. auditory analysis) Hughes & Foulkes BAAP 2014
- 10. 2.1 Data: /eɪ/ (FACE) 10 • 8 tokens per speaker – excluded pre-/l/, adjacent /r/ • formant trajectories fitted with corrected cubic polynomial curves – coefficients used as input data for LRs N=101 Professional Non-professional Younger (18-35) 29 24 Older (35-70) 25 23 Hughes & Foulkes BAAP 2014
- 12. 2.2 Method Hughes & Foulkes BAAP 2014 12 • structure: – 1 set of test data (multiple pairs of same- (SS) and different-speaker (DS) pairs of samples – multiple sets of reference data • matched with test data for social factor of interest • mismatched with test data for social factor of interest • mixed: no control over social factor of interest • LRs computed using MVKD (Aitken & Lucy 2004) – transformed using base-10 logarithm (0 = neutral evidence)
- 13. 2.3 Experiment 1: class-only Hughes & Foulkes BAAP 2014 13 • test set = 22 professionals • reference sets: Condition Speakers Hd: “it wasn’t the suspect, it was… Matched 40 prof another professional…” Mixed 20 prof/ 20 non-prof another man…” Mismatched 40 non-prof another non-professional…” ✓ (-) ✗
- 14. 2.3 Experiment 2: age-only Hughes & Foulkes BAAP 2014 14 • test set = 22 younger speakers • reference sets: Condition Speakers Hd: “it wasn’t the suspect, it was… Matched 40 young another young man…” Mixed 20 young/ 20 older another man…” Mismatched 40 older another older man…” ✓ (-) ✗
- 15. 2.3 Experiment 3: class & age Hughes & Foulkes IAFPA 2013 15 • test set = 29 young professionals • reference sets: Condition Speakers Hd Matched 20 young prof …another young professional…” Mixed 5 young prof/5 old prof/ 5 young non-prof/ 5 old non-prof …another man…” Mismatched (both) 20 older non-prof …another older non- professional…” Mismatched (class-only) 20 young non-prof …another young non- professional…” ✓ (-) ✗ ✗
- 16. 16 Hughes & Foulkes BAAP 2014 16 Raw LR Log10 LR Verbal expression >10000 4à5 Very strong evidence 1000à10000 3à4 Strong evidence 100à1000 2à3 Moderately strong evidence 10à100 1à2 Moderate evidence 1à10 0à1 Limited evidence 1à0.1 0à-1 Limited evidence 0.1à0.01 -1à-2 Moderate evidence 0.01à0.001 -2à-3 Moderately strong evidence 0.001à0.0001 -3à-4 Strong evidence <0.0001 -4à-5 Very strong evidence Champod and Evett (2000) Hp Hd
- 17. Hughes & Foulkes BAAP 2014 17 Matched Mismatched Mixed -7 -6 -5 -4 -3 -2 -1 0 1 DS SS DS SS DS SS Log10LR 3.1 Results: class-only
- 18. Hughes & Foulkes BAAP 2014 18 Matched Mismatched Mixed -7 -6 -5 -4 -3 -2 -1 0 1 DS SS DS SS DS SS Log10LR 3.1 Results: class-only But quite substantial differences for certain high magnitude DS LRs
- 19. Matched Mismatched Mixed -4 -3 -2 -1 0 1 DS SS DS SS DS SS Log10LR 3.2 Results: age-only
- 20. Matched Mismatched Mixed -4 -3 -2 -1 0 1 DS SS DS SS DS SS Log10LR 3.2 Results: age-only Again big differences in individual SS & DS LRs according to reference set
- 21. 3.3 Results: performance 21 • Log LR cost (Cllr): penalises the system for high magnitude contrary to fact LRs – errors: false misses (SS as DS) and false hits (DS as SS) – but an incorrect log LR close to 0 is much less important than an incorrect LR with very high magnitude – Cllr ideally close to 0 when no errors are made Hughes & Foulkes BAAP 2014
- 23. 4.0 Discussion Hughes & Foulkes BAAP 2014 23 • magnitude of LRs and system validity affected by different delimitations of class and age – even relatively subtle variability can be reflected in strength of evidence • effects on the LRs most marked for outlying comparisons – overestimation of strength of evidence by up to 3 log10 orders of magnitude – DS pairs more sensitive than SS pairs
- 24. 4.0 Discussion 24 • effects on Cllr appear systematic : – mixed = under optimistic Cllr relative to matched – mismatch = over optimistic Cllr relative to matched – age a more significant factor than class • ‘getting it wrong’ more problematic than ‘keeping it general’ – distributions of LRs and Cllr closer to baseline in mixed condition than in mismatched Hughes & Foulkes BAAP 2014
- 25. 5.0 Conclusion 25 • essential to consider the relevant population with respect to (socio)linguistic dimensions – between-S variation = complex and multi- dimensional – important to be aware of how sources of variation interact with each other (e.g. age and class) • understanding the sources of variability ensures estimates of strength of evidence are more meaningful Hughes & Foulkes BAAP 2014