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Establishing typicality: a closer look at individual formants

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Hughes, V. (2013) Establishing typicality: a closer look at individual formants. Paper presented at 165th Meeting of the Acoustical Society of America (ASA)/ 21st International Congress on Acoustics (ICA), Montreal, Quebec. 2-7 June 2013.

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Establishing typicality: a closer look at individual formants

  1. 1. Establishing typicality: a closer look at individual formants Vincent Hughes Department of Language and Linguistic Science
  2. 2. • traditional view = F1 and F2 responsible for phonetic contrast (Peterson 1959, Ladefoged2006) – less freedom for individual variation within homogeneous populations • F3 more ‘speaker-specific’ than lower formants – associated with resonances in smaller cavities of the vocal tract (Rose 2002) – listeners less sensitive to differences/ variation in F3 1. Introduction ICA/ASA/CAA Montreal, Canada 3rd June 2013 2
  3. 3. • pervious studies in British English offer evidence in support of the ‘traditional view’ – SSBE(predominantly using DyVis (Nolan et al 2009)) – but based almost exclusively on posterior-based discriminant analysis (DA) – using relatively small N speakers 1. Introduction ICA/ASA/CAA Montreal, Canada 3rd June 2013 3
  4. 4. 1. Introduction 4 Constraints on the speaker-space (Nolan 1991): i. anatomical factors - size/ shape of the vocal tract ii. articulatory factors - range of potential movement iii. phonological factors - adjacent sounds/ long-term resonances iv. socio-indexical factors - those which make the community homogeneous ICA/ASA/CAA Montreal, Canada 3rd June 2013
  5. 5. 1. Introduction 5 Hypotheses: • DA with small N speakers overestimates the separation of individuals in the speaker space • consistent with the ‘traditional view’, in LR- based discrimination F3 outperforms F1 and F2 • the constraints on variation in the speaker space are variety and phoneme specific ICA/ASA/CAA Montreal, Canada 3rd June 2013
  6. 6. 6 • dynamic time-normalised measures of /aɪ/ (F1, F2, F3) measurements per formant (at +10% steps) 2. Data ICA/ASA/CAA Montreal, Canada 3rd June 2013
  7. 7. 2. Data 7 • DyVis (Nolan et al 2009) database: – male SSBE speakers – university students (aged 18-25) – mock police interview recordings – 11-19 tokens per speaker (mean = 15) • data reduction using quadratic polynomial fits – coefficients used as input data cbxaxy ++= 2 ICA/ASA/CAA Montreal, Canada 3rd June 2013
  8. 8. 2. Data 8 ICA/ASA/CAA Montreal, Canada 3rd June 2013 500 1000 1500 2000 2500 25 50 75 +10% Step Frequency(HZ) F1 F2 F3 2200 2000 1800 1600 1400 1200 1000 800 800700600500400300 F2(Hz) F1(Hz) FLEECE /i:/ GOOSE /u:/ NORTH /ɔ:/ TRAP /æ/
  9. 9. 3.1 DA-based testing 9 • 89 speakers: – 13 tokens per speaker – N tokens needs to be > N input predictors (polynomial coefficients) • cross-validated classification rates (%) for each formant – starting with 5 speakers and increasing by 5 speakers in each condition up to 89 speakers ICA/ASA/CAA Montreal, Canada 3rd June 2013 p(H|E)
  10. 10. 3.1 DA-based testing 10 ICA/ASA/CAA Montreal, Canada 3rd June 2013
  11. 11. 3.2 LR-based testing 11 • test set = 20 speakers – mock DS and KS (where true outcome is known) – 20 same-speaker pairs/ 380 different-speaker pairs • reference set = 77 speakers • 10 tokens per speaker • multivariate kernel density formula (Aitken and Lucy 2004) used to compute LRs • performance assessed in terms of equal error rate (EER) and log LR cost (Cllr) ICA/ASA/CAA Montreal, Canada 3rd June 2013 p(E|Hss) p(E|Hds) LR =
  12. 12. 3.2 Results ICA/ASA/CAA Montreal, Canada 3rd June 2013 F1 F2 F3 F1, F2 and F3 12
  13. 13. 3.2 Results 13 ICA/ASA/CAA Montreal, Canada 3rd June 2013 F1, F2 and F3 F3 F1 F2
  14. 14. 4. Discussion 14 DA with small N speakers overestimates the separation of individuals in the speaker space (i) discriminatory performance of all formants decreases - largest decrease = F3 (from 60% to 8.3%) - although consistently better than chance (ii) comparative performance of individual formants differs - F3 outperforms F1 and F2 between 5 and 45 speakers - F1 generally outperforms F2 and F3 with > 45 speakers ICA/ASA/CAA Montreal, Canada 3rd June 2013
  15. 15. 4. Discussion 15 consistent with the ‘traditional view’, in LR-based discrimination F3 outperforms F1 and F2 • F3 marginally better than F1 and F2 • marginal differences in SS strength of evidence – F3 > F1 > F2 on average (all ‘limited’ support for Hss) • F3 achieves highest DS LRs – but on average only 1.23 times > F1 LRs – F1 > F2 • better system performance (EER/ Cllr) with F3 ICA/ASA/CAA Montreal, Canada 3rd June 2013
  16. 16. between-speaker variation within-speaker variation (SD of means) (mode of SDs) • variance ratio = 181Hz/157Hz = 1.153 • !! single style, single recording per speaker, single interlocutor, studio quality !! 16
  17. 17. 4. Discussion 17 the constraints on variation in the speaker-space are variety and phoneme specific • correlation between F2 and F3 due to high-front offset (systematicityin F3) • no dynamic differences in F3 • Stevens and French (2012):VQ data for same DyVis speakers – 93% fronted tongue body – 86% fronted tongue tip/blade – affecting volume of the front cavity à F3 ICA/ASA/CAA Montreal, Canada 3rd June 2013
  18. 18. 5. Conclusion 18 • discriminatory potential is not universal – traditional view of individual formant performance is over simplistic – but discriminatory potential is (to some extent) predictable • reliance on generalisation inappropriate for casework – speakers differ from each other in different ways (Rose 2006) – need for more socio-phonetically informed decisions about which parameters to analyse ICA/ASA/CAA Montreal, Canada 3rd June 2013
  19. 19. Thanks! Questions? Acknowledgements: Paul Foulkes, Peter French, Dom Watt, Erica Gold, FSS Research Group (York), Ashley Brereton (University of Liverpool)

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