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Correlations within- and between-speech parameters in Standard Southern British English

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Gold, E. and Hughes, V. (2014) Correlations within- and between-speech parameters in Standard Southern British English. Paper presented at International Association for Forensic Phonetics and Acoustics (IAFPA) Conference, Universität Zürich, Switzerland. 31 August-3 September 2014.

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Correlations within- and between-speech parameters in Standard Southern British English

  1. 1. The correlation structure of speech parameters in Southern Standard British English Erica Gold and Vincent Hughes Department of Language and Linguistic Science IAFPA Conference Universität Zürich 31st August – 3rd September 2014
  2. 2. Outline • Introduction • Data • Methodology • Systems: 1. Naïve Bayes 2. Theoretical correlations 3. Broad Empirical System 4. Narrow Empirical System • Results • Future Work 2
  3. 3. Introduction • IAFPA funded project (2013-14): Identifying correlations between speech parameters for forensic speaker comparisons • Aims: – analyse the correlations between parameters common in forensic speaker comparison (FSC) – compare empirical correlations with those predicted by theory – develop new ways of accounting for correlations in LR-based FSC 3
  4. 4. Introduction • Why are correlations so important? – Naïve Bayes: LRs from separate pieces of evidence can be combined as long as they are independent of each other – Speaker comparison based on the combined strength of individual parameters • Highly multidimensional form of expert evidence • Complex correlation structure – Not accounting for correlations can overestimate the strength of evidence and potentially lead to miscarriages of justice 4
  5. 5. Introduction Accounting for correlations in LR-based FSC • Logistic regression fusion (Brümmer et al. 2007) = back-end processing – Considers correlations in LRs from different parameters using the same comparisons – Logistic regression model then applied to suspect and offender LRs – Developed in ASR for combining the results of different systems 5
  6. 6. Introduction • Very little research considering front-end processing for FSC: – Examining correlations in the raw data rather than in the resulting LRs – Gold and Hughes (2012): pilot study • Quantifying correlations between a small subset of FSC parameters • Identifying predictable and unpredictable, significant and non-significant correlations 6
  7. 7. Introduction • In this talk we will: – Explore the theoretical and empirical correlations across a number of FSC parameters using a homogeneous set of speakers – Consider different approaches to quantifying the correlations between parameters – Build 4 LR-based systems using different methods of accounting for the correlations • Compare system output in terms of the magnitude of the LRs and system validity 7
  8. 8. Data • DyViS database (Nolan et al. 2009) – Word-initial /t/ & /k/: VOT (ms) and closure dur (ms) – Articulation rate (AR): Mean syllables/ sec – f0: Mean and Standard Deviation – Long-term formant distributions (LTFD): F1-F4 – UH & UM (hesitation markers): F1-F3 @ midpoint – PRICE: F1-F3 @ +20% and +80% steps – TRAP, GOOSE & NORTH: F1-F3 @ midpoint *all data from spontaneous speech8
  9. 9. Methodology • Data collected for 36 speakers – Development (training) data = 18 speakers – Test data = 18 speakers – Reference data = 34 speakers (36 – sus & off) • Cross-validated LR scores computed for development and test data using Matlab implementation (Morrison 2007) of MVKD formula (Aitken and Lucy 2004) – Parameters analysed as multivariate data* and as univariate data using individual elements 9
  10. 10. Methodology • Test scores (for each parameter) converted to LLRs using logistic regression calibration weights generated from development scores (Brümmer and du Preez 2006) • Output: – Parallel sets of 18 SS and 306 DS calibrated LLRs – Multivariate data = 11 parameters (/sets) – Univariate data = 32 parameters (/sets) 10
  11. 11. System 1: Naïve Bayes • Naïve Bayes: combining parameters which are assumed to be independent • Added all of the LLRs for each SS and DS comparison for each parameter – No correlations taken into account, hence the “naïve” approach • System parameters: – /t/, /k/, AR, f0, LTFD, UM, UH, PRICE, TRAP, GOOSE, and NORTH 11
  12. 12. System 2: Theoretical • Based on theory and previous empirical research in ling-phon – Considering correlations between individual elements 12
  13. 13. System 2: Theoretical • Based on theory and previous empirical research in ling-phon 13 • Temporal parameters predicted to be correlated • /k/ VOT + closure > /t/ • /t, k/ dependent on AR (faster speech = shorter durations)
  14. 14. System 2: Theoretical • Based on theory and previous empirical research in ling-phon 14 • Source & filter: independent? • Assmann and Nearey (2007) • Link with vocal effort (Lombard speech)?
  15. 15. 15 System 2: Theoretical
  16. 16. System 2: Theoretical • Including only parameters which are predicted to be independent • System parameters = two options: (a) Incl. F1 & remove f0 • AR, LTFD (F1-F4) (b) Incl. f0 & remove F1 • AR, f0, LTFD (F2-F4) 16
  17. 17. System 3: Broad Empirical • Attempt to account for empirical correlations in the raw data – Treating parameters as the sum of their parts and considering the correlations between these – Hence ‘broad’ empirical system • Means based on raw data calculated for each speaker for each element of each parameter • Euclidean Distances (EDS) calculated for each parameter as whole (e.g. f0 = f0 mean + f0 standard deviation) 17
  18. 18. System 3: Broad Empirical • Spearman Correlation matrix between all parameters generated based on EDs • Conservative threshold for determining whether parameters are ‘correlated’ – r2 > 0.06 (r > 0.25) – ensures that all correlations are captured (even if it means including independent parameter) 18
  19. 19. 19 Spearman r2
  20. 20. 20 Spearman r2
  21. 21. 21 Spearman r2
  22. 22. 22 Spearman r2 X d = 0.238 d = 0.505
  23. 23. 23 Spearman r2 X d = 0.288 d = 0.319 X
  24. 24. 24 Spearman r2 X d = 0.288 X d = 0.238 X
  25. 25. 25 Spearman r2 X d = 0.288 X d = 0.238 X • System parameters: - /t/, /k/, AR, f0, UM, TRAP, GOOSE, and NORTH
  26. 26. System 4: Narrow Empirical • Alternative approach to account for empirical correlations in the raw data – Treating parameters in terms of their individual parts – Hence ‘narrow’ empirical system • Means based on raw data calculated for each speaker for each element of each parameter • Spearman Correlation matrix between all elements generated based on raw means – Conservative threshold again implemented for correlations r2 > 0.06 (r > 0.25) 26
  27. 27. Spearman r2 27
  28. 28. System 4: Narrow Empirical • Large number of correlations in the raw data – Reflecting complex correlation structure of speech as evidence – Both predicted and unpredicted correlations • Same procedures as in System 3 for choosing correlated parameters • System parameters: – LTFD4, UM F2, NORTH F2, GOOSE F3, TRAP F2, LTFD3, /t/ VOT & DUR, UH F1, and f0 mean 28
  29. 29. Results System Comparison Median LLR Cllr EER 1: Naïve Bayes SS 9.3452 0.1735 0.00% 1: Naïve Bayes DS -38.7192 2a: Theory SS 1.0790 0.2696 6.70% 2a: Theory DS -5.3891 2b: Theory w/f0 SS 1.9765 0.0818 0.82% 2b: Theory w/f0 DS -12.9326 3: Broad SS 6.0610 0.5346 5.56% 3: Broad DS -27.8995 4: Narrow SS 3.4329 0.0217 0.16% 4: Narrow DS -23.6015 29
  30. 30. Implications • Naïve Bayes: Overestimates the strength of evidence (as seen in the median LLRs) – Essentially doubling evidence, since we have shown that correlated parameters exist in our data set • Theory-Based: Underestimates the strength of evidence (as seen in the median LLRs) – Considerable difference in the parameters which are predicted to be significant and those which are empirically significant – Ignores parameters which are actually independent of those parameters included in the system – Overly conservative? 30
  31. 31. Implications • Highly complex correlation structure – Differences between predictions based on theory and those empirical correlations in the data – Differences in how these correlations can be quantified and applied in LR-based FSC • Empirical Systems: – Front-end processing used to influence the creation of the system – Narrow empirical system outperforms Naïve Bayes and Theory in terms of validity (Cllr) 31
  32. 32. Implications • LR-based FSC: – Prefer a front-end processing technique for combining parameters may want to test the correlations and then combine based on results • Non LR-based FSC: – Working in British English may consider the results we have provided here today for the 11 parameters 32
  33. 33. Future Work • Compare systems in terms of reliability (as well as validity) • Alternative front-end processing approaches for capturing correlations in LR-based FSC • Compare correlations in raw data with correlations in LRs – Assess theoretical issues underlying the application of fusion – Front-end processing vs. back-end processing • Expand our project and incorporate additional parameters 33
  34. 34. Acknowledgements: Funding: IAFPA(Research Grant 2013-2014) Katherine Earnshaw, Natalie Fecher, Katharina Klug, Sophie Wood, Nathan Atkinson, Jade King, Paul Foulkes, Peter French, Dominic Watt 34 Thanks! Questions?

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