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Establishing	typicality:	a	closer	look	
at	individual	formants
Vincent	Hughes
Department	of	Language	and	Linguistic	Science
• traditional	view	=	F1	and	F2	responsible	for	
phonetic	contrast	(Peterson	1959,	Ladefoged2006)
– less	freedom	for	indivi...
• pervious	studies	in	British	English	offer	evidence	
in	support	of	the	‘traditional	view’
– SSBE(predominantly	using	DyVi...
1.	Introduction
4
Constraints	on	the	speaker-space	(Nolan	1991):
i. anatomical	factors
- size/	shape	of	the	vocal	tract
ii...
1.	Introduction
5
Hypotheses:
• DA	with	small	N	speakers	overestimates	the	
separation	of	individuals	in	the	speaker	space...
6
• dynamic	time-normalised measures	of	/aɪ/	(F1,	F2,	
F3)	measurements	per	formant	(at	+10%	steps)
2.	Data
ICA/ASA/CAA	
M...
2.	Data
7
• DyVis (Nolan	et	al	2009)	database:
– male	SSBE	speakers	
– university	students	(aged	18-25)
– mock	police	inte...
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
22...
3.1	DA-based	testing
9
• 89	speakers:
– 13	tokens	per	speaker
– N	tokens	needs	to	be	>	N	input	predictors	
(polynomial	coe...
3.1	DA-based	testing
10
ICA/ASA/CAA	
Montreal,	Canada
3rd	June	2013
3.2	LR-based	testing
11
• test	set =	20	speakers
– mock	DS	and	KS	(where	true	outcome	is	known)
– 20	same-speaker	pairs/	3...
3.2	Results
ICA/ASA/CAA		Montreal,	Canada		3rd	June	
2013
F1
F2
F3
F1, F2 and F3
12
3.2	Results
13
ICA/ASA/CAA		Montreal,	Canada		3rd	June	
2013
F1, F2 and F3
F3
F1
F2
4.	Discussion
14
DA	with	small	N	speakers	overestimates	the	
separation	of	individuals	in	the	speaker	space
(i) discrimina...
4.	Discussion
15
consistent	with	the	‘traditional	view’,	in	LR-based	
discrimination	F3	outperforms	F1	and	F2
• F3	margina...
between-speaker	variation within-speaker	variation
(SD	of	means) (mode	of	SDs)
• variance	ratio	=	181Hz/157Hz	=	1.153
• !!...
4.	Discussion
17
the	constraints	on	variation	in	the	speaker-space	
are	variety	and	phoneme	specific
• correlation	between...
5.	Conclusion
18
• discriminatory	potential	is	not	universal
– traditional	view	of	individual	formant	performance	is	
over...
Thanks!
Questions?
Acknowledgements:	Paul	Foulkes,	Peter	French,	
Dom	Watt,	Erica	Gold,	FSS	Research	Group	(York),	
Ashley...
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Establishing typicality: a closer look at individual formants

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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