Variability of Formant Measurements – Part 2

1. Variability of Formant Measurements – Part 2 Philip Harrison J P French Associates & Department of Language & Linguistic Science, York University IAFPA 2006 Annual Conference Göteborg, Sweden

2. Summary • Briefly recap previous analysis & last year’s presentation • New analysis & results • PhD research • Questions l

3. Study • Aim: Investigate the variability of formant measurements which exists both within and between different software programs currently used in the field of forensic phonetics. • 3 programs – Praat, Multispeech & Wavesurfer • 3 analysis parameters – LPC order, analysis (frame/window) width, pre-emphasis • Word list – 5 vowel categories – 6 tokens per category – read 3 times – total = 90 tokens • 2 speakers – Peter French & me • 2 simultaneous recordings – microphone & telephone l

4. Results & Analysis • Scripts used to obtain 37,260 individual formant measurements using LPC formant trackers • Analysis – microphone data only • Initial observations of raw formant data • Quantitative analysis of results • Statistical analysis l

5. FLEECE TRAP PALM GOOSE SCHWA My F1s from PraatLPC Variation l

6. The Plot Shows… • Scripts work – (used in fault finding) • Vowel categories clear • Greatest deviation – LPC orders 6 & 8 • Orders 10 to 18 very similar for FLEECE, GOOSE & SCHWA • Generated many more plots for all formants, parameters & software • Lots of variation • Difficult to interpret l

7. Quantitative Analysis • Quantitative Difference Analysis • No absolute measurement to compare formants with – outcome of analysis, not directly comparable with acoustic reality • Difference calculated between value obtained with default analysis settings • Absolute difference calculated for each formant then averaged by vowel category • Shows variation between two analyses l

8. Observations • Numerical analysis confirmed impression from plots • Clear differences between vowel categories, speakers, formants, software & settings • Complex set of results with no clear patterns l

9. Statistical Analysis • Paired t-test between measurements from default settings and varied settings for each vowel category • Null hypothesis – altering analysis settings  no effect • Exp hypothesis – altering analysis settings  effect • Number of significant ‘hits’ summed – max 15 • Higher number = greater variation in formant measurements • 2 significance levels – 0.01 & 0.05 l

10. Conclusions • Hoped to have clear patterns, able to produce set of guidelines/recommendations • Patterns only at specific, detailed level • Very clear that many factors affect formant measurements • No software is obviously better than others • Care should be taken when measuring formants l

11. New Work!!! • Initial data contained obviously incorrect measurements • Discard measurements – criterion? • Determine acceptable band • Spectrograms – no • Formant bandwidths – no (attempted) • LPC tracker & spectrogram – no (attempted) • Spectrum of selection – yes but still encountered problems • Band limit 300 Hz – impressionistic l

12. Spectrum Measurements • Used to determine centre of 300 Hz acceptable band • Spectrum with 260 Hz bandwidth – same as default spectrogram • Measured peaks F1, F2 & F3 • Issues/problems • Windowed -> biased to centre of selection • Formant peaks not always clear – some tokens ignored • Double peaks – highest peak measured l

13. Analysis of Accepted Measurements • Analyse LPC variation only – other parameters more stable – not altered • No accurate reference which raw measurements can be judged against • Accepted results provide indication of accuracy & consistency • Clear patterns in accepted formants • Condense results – % accepted per vowel category l

14. Plot of Accepted Results l

15. Me Microphone Accepted Praat Multispeech Wavesurfer F1 F2 F3 l

16. Me Telephone Accepted Praat Multispeech Wavesurfer F1 F2 F3 l

17. JPF Microphone Accepted Praat Multispeech Wavesurfer F1 F2 F3 l

18. JPF Telephone Accepted Praat Multispeech Wavesurfer F1 F2 F3 l

19. General Patterns • Praat & Multispeech – bell curves • Most consistent setting – P 10, MS 10 to 14 • Curves shifted to left (lower LPC) for phone • Wavesurfer – horizontal • Different behaviour to Praat & Multispeech • Some very weak results – especially F3 • For me better results for phone recording (also true for Praat & Multispeech) • Most consistent setting Praat LPC 10 • Again variation across vowel category, speaker, formant, software & condition l

20. Microphone vs Telephone • Künzel (2001): • Landline phone vs microphone • Largest F1 difference in region of 14% for close vowels • Byrne & Foulkes (2004): • GSM mobile phone vs microphone • F1 average 29% higher for GSM • Not big differences for F2 & F3 • Current data (spectral comparisons) – only 2 speakers l

21. Comparison Tables Me JPF l

22. General Observations • LPC tracks for phone recordings more stable, easier to measure • Less ‘information’ above F3 • Possibly pre-filter recordings? • Different LPC orders produce better tracks for different formants of the same token • Contradicts my previous advice to keep LPC setting constant across vowel categories l

23. PhD Next Steps • Use synthesised speech • Formant values specified • Repeat software experiments • Other factors to investigate • Pitch • Voice quality • Interaction of analysis parameters l

24. Other Potential Areas of Investigation for PhD • Effects of GSM coding & transmission • Acoustic environments • Pseudo-formants – source??? • Mouth/telephone distance & orientation • Any other ideas…? l

25. Questions ? Thanks to Peter French & Paul Foulkes l