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Issues with speech as evidence

Hughes, V. (2013) Issues with speech as evidence. Vice-Chancellor’s visit, Department of Language and Linguistic Science, University of York, UK. 2 August 2013. (INVITED TALK)

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Issues with speech as evidence

  1. 1. Issues with speech as evidence Vincent Hughes
  2. 2. Forensic speech science = application of linguistic/ phonetic/ acoustic analysis to legal proceedings (investigatory/ evidential) Introduction
  3. 3. n forensic voice comparison (FVC) = voice of criminal (disputed) vs. voice of suspect (known) n disputed (DS) = threatening phone calls, bomb threat... n known (KS) = police interview recording Forensic voice comparison
  4. 4. n no such thing as a voiceprint (equivalent to a fingerprint) n range of parameters analysed (Gold and French 2011): n segmental (vowels, consonants) n suprasegmental (f0, intonation, articulationrate) n higher-order linguistic (lexical choice, syntax) n voice quality/ vocal setting Forensic voice comparison
  5. 5. Expressing conclusions ✗ but this is a judgment of guilt…
  6. 6. n gradient assessment of strength of evidence n e.g. LR = 5, evidence is 5 times more likely assuming it’s the same-speaker Likelihood ratio p(E|Hp) p(E|Hd) likelihood ratio (LR) = ✓
  7. 7. n LR = similarity and typicality n typicality = dependent on patterns in the relevant population (Aitken and Taroni 2004) n quantified relative to a sample of that population n distributions modelled statistically to generate numerical output n framework used in other forensic sciences (e.g. DNA, glass fragments…) Likelihood ratio
  8. 8. n but speech isn’t like other forms of forensic evidence n it’s complex and highly multidimensional n numerous sources of within- and between-sp variation n current applications of the LR to speech only control language (region) and sex in the relevant population Issues
  9. 9. Issues 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… n what to control in the relevant population and how narrowly? n paradox: without knowing who the offender is we can’t know (for sure) what linguistic population he comes from
  10. 10. Match = relevant population controlled narrowly n baseline results for comparison with other conditions n /u:/ (goose, boot, suit): regional variation n /aɪ/ (price, bite, site): regional variation n /eɪ/ (face, bait, state): social class, age General results
  11. 11. Match = relevant population controlled narrowly General results
  12. 12. Mismatch = relevant population controlled narrowly but wrongly General results n overestimation of strength of evidence for compared with the match condition n samples highly atypical relative to the relevant population n denominator of LR much lower
  13. 13. Mismatch = relevant population controlled narrowly but wrongly General results (i) overestimation of support for the defence (ii) pairs are so atypical they offer support for the prosecution! (different speakers were the evidence supports guilt)
  14. 14. Mixed = relevant population not controlled for sources of variation (region/ age/ class) General results n underestimationof strength of evidence n wider spread to variation n increases the value of the denominator
  15. 15. n LRs substantially affected by differences in the relevant population n extent dependent on linguistic variable/ social variable under analysis n ‘getting it wrong’ worse than ‘keeping it general’ n knowing the socio-phonetics of the community of interest means strength of evidence is more meaningful Conclusions
  16. 16. n within-sp variation n p(E|Hp) is never 1 (unlike DNA) n between-sp variation n multiple data types n continuous/ discrete/ normal/ non-normal n speech parameters are highly correlated n how do we combine these into overall LR? n mismatch in real forensic recordings Issues with the application of the LR