1. Parsing acoustic variability as a mechanism for feature abstraction Jennifer Cole Bob McMurray Gary Linebaugh Cheyenne Munson University of Illinois University of Iowa www.psychology.uiowa.edu/faculty/mcmurray

2. Phonetic precursors to phonological sound patterns Many phonological sound patterns are claimed to have precursors in systematic phonetic variation that arises due to coarticulation Assimilation –Vowel harmony from V-to-V coarticulation (Ohala 1994; Beddor et al. 2001) –Palatalization from V-to-C coarticulation (Ohala 1994) –Nasal Place assimilation (-mb, -nd, -ŋg) from C-to- C coarticulation (Browman & Goldstein 1991) Assimilation Epenthesis –Epenthetic stops from C-C coarticulation: sen[t]se (Ohala 1998) Assimilation Epenthesis Deletion –Consonant cluster simplification via deletion from C-C coarticulation: perfec(t) memory (Browman & Goldstein 1991)

3. The role of the listener Phonologization: when acoustic properties that arise due to coarticulation are interpreted by the listener as primary phonological properties of the target sound. generalization over variable acoustic input that results in a new constraint on sound patterning.

4. The role of the listener From V-to-V coarticulation … ɛ ʌ ɑ i

5. The role of the listener From V-to-V coarticulation … [… ɛ i …i…] ɛ ʌ ɑ i [… ɛ ɑ … ɑ …]

6. The role of the listener Perception may yield vowel assimilation [… ɛ i …i…] ɛ ʌ ɑ i [… ɛ ɑ … ɑ …] i ɑ

7. The role of the listener But – distinct factors can produce similar variants: [… ɛ i …i…] ɛ ʌ ɑ i [… ɛ ŋ…]

8. From perception to phonology What is the mechanism for mapping from continuous perceptual features to phonological categories? ɛ i mid and high central and front-peripheral ɛ ɑ mid and low central and back

9. From perception to phonology What is the mechanism for mapping from continuous perceptual features to phonological categories? ɛ i mid and high central and front-peripheral ɛ ɑ mid and low central and back The problem: The perceptual system is confronted with uncertainty due to variation arising from multiple sources. Yet, patterns of variation must get associated with individual features of the context vowel (e.g,. high, front) if coarticulation serves as a precursor to phonological assimilation. How do lawful, categorical patterns emerge from ambiguous, variable input? …the lack of invariance problem!

10. Our claims What is the mechanism for mapping from continuous perceptual features to phonological categories? Our claims: Variability is retained. Acoustic variability is parsed into components related to the target segment and the local context. Feature abstraction through parsing. Acoustic parsing provides a mechanism for the emergence of phonological features from patterned variation in fine phonetic detail.

11. Variability is retained Listeners are sensitive to fine-grained acoustic variation. (Goldinger 2000; Hay 2000; Pierrehumbert 2003)  Variability is retained, not discarded Consistent with exemplar models of the lexicon, phonetic detail is encoded and stored, and can inform subsequent categorization of new sound tokens.

12. Variability due to coarticulation is subtracted to identify the “underlying” target sound. (Fowler 1984; Beddor et al. 2001, 2002; Gow 2003) Variability is retained Variability is useful for the identification of sounds in contexts of coarticulation. The perceptual system uses information about variability to identify a sound and its context, in parallel. Variability due to coarticulation is exploited to facilitate perception. -- Listeners benefit from the presence of anticipatory coarticulation in predicting the identity of the upcoming sound. (Martin & Bunnell 1982; Fowler 1981, 1984; Gow 2001, 2003; Munson, this conference)

13. Variability and perceptual facilitation Perceptual facilitation from V-to-V coarticulation is expected to occur only if: The effects of coarticulation are systematic—an influencing vowel conditions a consistent acoustic effect on target vowels; The listener can recognize coarticulatory effects on the target vowel; The listener can isolate the effects of context vowel from other sources of variation, and attribute those effects to the context vowel.

14. Feature abstraction through parsing More specifically…under coarticulation of vowel height and backness, The listener must parse out the portion of the variance in F1 and F2 that is due to coarticulation, and base their perception of the target vowel on the residual values. Acoustic parsing isolates the effects of context vowel on F1 and F2.

15. Feature abstraction through parsing The parsed acoustic variance defines features of the context vowel, over which new generalizations can be formed.  phonologization [ ɛ ] + [i] [ ɛ ] +[high] ɛiɛi

16. Feature abstraction through parsing The parsed acoustic variance defines features of the context vowel, over which new generalizations can be formed.  phonologization [ ɛ ] + [i] [ ɛ ] +[high] phonologized to [i] i

17. Feature abstraction through parsing The parsed acoustic variance defines features of the context vowel, over which new generalizations can be formed.  phonologization Question: Why phonologization? If target and context vowels can both be identified from the fine phonetic detail…. What’s the force driving phonologization?

18. Testing the model The acoustic parsing model of speech perception requires that there is a robust and systematic pattern of acoustic variation from V-to-V coarticulation. This paper: we present supporting evidence from an acoustic study of coarticulation. We examine a range of V-to-V coarticulatory effects in VCV contexts that cross a word boundary, where coarticulation cannot be attributed to lexicalized phonetic patterns.

19. Key Questions Extent of phenomenon Does V-to-V coarticulation cross word boundaries? Does V-to-V coarticulation affect both F1 and F2? Relative strength of V-to-V effects vs. other forms of coarticulation? Usefulness of phenomenon How could V-to-V effects translate to perceptual inferences? Is the information by V-to-V coarticulation different when other sources of variation are explained?

20. Methods Target vowels: ɛ ʌ Measure coarticulation Context vowels: i æ ɑ Induce Coarticulation i æ ɑ ʌ ɛ /u/ excluded from contexts (rounded + fronted) intervening consonant varied in - place (labial, coronal, velar) - voicing - / ɛ g/ excluded (tends to be raised)

21. Methods bedactortechafternoonwebaddict eagleeveningecologist evergreenelevatoreducator ostrichOxygenOffer wetAfrodeckalligatorstepAdmiral Easter Bunnyeaster basketeast Eskimoelephantexit Oxenoctopusobstacle mudapplebugastronautpubadvertisement eaterevileasel umpireunderwearundergrad observationopticianoperator cutabdomenduckathletecupappetizer evenlyeatingeavesdropping onionusheroven Oliveofficeroccupant

22. Methods 10 University of Illinois students. 48 phrases x 3 repetitions. Sentences embedded in neutral carrier sentences / ɛ / He said ‘_______’ all the time / ʌ / I love ‘_______’ as a title Coding F1, F2, F3 - Converted to Bark for analysis LPC (Burg Method) Outliers / misproductions inspected by hand

23. Analysis Target x Voicing x Context F1F2 Voicing p=.033p=.001 Targetp=.005p=.001 Contextp=.001p=.001 Interactionsn.s.n.s. Target x Place x Context F1F2 Placen.s.p=.001 Targetp=.01p=.001 Contextp=.001p=.001 Interactionssomesome V-to-V coarticulation crosses word boundaries. Clear effects of coarticulatory context on both F1 and F2.

24. Analysis Male Female A lot of unexplained variance… How does the perceptual system “get to” the V- to-V coarticulation? How useful is V-to-V coarticulation? Does accounting for other sources of variance in the signal improve the usefulness of V-to-V?

25. Strategy Need to systematically account for sources of variance prior to evaluating V-to-V coarticulation. F2 ɛʌ 1431 hz1801 hz i ɑ ? ɑ -coarticulated ɛ ? or i-coarticulated ʌ ?

26. Strategy F2 ɛʌ 1431 hz1801 hz i ? A slightly i-coarticulated ɛ ? or A really i -coarticulated ʌ ? Need to systematically account for sources of variance prior to evaluating V-to-V coarticulation.

27. Strategy F2 ɛʌ 1431 hz1801 hz i ɑ ? If you knew the category… If ʌ, then expect i If ɛ then expect ɑ ? - ʌ : Positive (more i-like) ? - ɛ : Negative (more ɑ -like) F2 ? – F2 category = coarticulation direction Need to systematically account for sources of variance prior to evaluating V-to-V coarticulation.

28. Strategy Target – F2 ? = coarticulation direction Strategy: 1) Compute mean of a source of variance 2) Subtract that mean from F1/F2 3)Residual is coarticulation direction. 4)Repeat for each source of variance (speaker, target vowel, place, voicing).

29. Strategy F1 predicted =  1 * target +  0 If target = 0 for / ʌ / and 1 for / ɛ /… ʌ ) F1 predicted =  1 * 0 +  0 Mean / ʌ / =  0 ɛ ) F1 predicted =  1 * 1 +  0 Mean / ɛ / =  0 +  1 Hierarchical Regression can do exactly these things. 1) Compute mean of a source of variance

30. Strategy Hierarchical Regression can do exactly these things. 1)Compute mean of a source of variance. 2)Subtract that mean from F1/F2 3)Residual is coarticulation direction. Residual F1 actual - F1 predicted = F1 actual - (  1 · target +  0 ) ʌ ) Resid target = F1 actual -  0 ɛ ) Resid target = F1 actual - (  0 +  1 )

31. Strategy Hierarchical Regression can do exactly these things. 1)Compute mean of a source of variance. 2)Subtract that mean from F1/F2 3)Residual is coarticulation direction. 4)Repeat for each source of variance (speaker, target vowel, place, voicing). Resid target =  2 * Place +  0 Resid place =  3 * Voicing+  0 F1 =  0 * Target+  0 Resid voicing =  4 * V-to-V +  0

32. Strategy Construct a hierarchical regression to systematically account for known sources of variance from F1 and F2 Speaker Target vowel Place (intervening C) Voicing (intervening C) Interactions between target, place & voicing After partialing out these factors, how much variance does vowel context (V-to-V) account for?

33. Regression F2 1) Raw Data Male Female

34. Regression F2 1) Raw Data Partialed Out 2) Subject ʌ ɛ

35. Regression F2 1) Raw Data Partialed Out 2) Subject 3) Target Vowel

36. Regression F2 1) Raw Data Partialed Out 2) Subject 3) Target Vowel 4) Consonant

37. Regression F2 1) Raw Data Partialed Out 2) Subject 3) Target Vowel 4) Consonant 5) Interactions

38. Regression F1 StepVariablesR 2 change P 1Subjects (10).824***

39. Regression F1 StepVariablesR 2 change P 1Subjects (10).824*** 2Vowel.009*** 3Voicing.018*** 4Place (2).003**

40. Regression F1 StepVariablesR 2 change P 1Subjects (10).824*** 2Vowel.009*** 3Voicing.018*** 4Place (2).003** 5Vowel x Voicing.000- 6Vowel x Place (2).002* 7Voicing x Place (2).012*** Total R 2 =.884 Post-hoc analysis: height only.

41. Regression F1 StepVariablesR 2 change P 1Subjects (10).824*** 2Vowel.009*** 3Voicing.018*** 4Place (2).003** 5Vowel x Voicing.000- 6Vowel x Place (2).002* 7Voicing x Place (2).012*** 8ContextVl (3).012*** 9ContextVL interactions (12).003- Total R 2 =.884 Post-hoc analysis: height only.

42. Regression F2 StepVariablesR 2 change P 1Subjects (10).409***

43. Regression F2 StepVariablesR 2 change P 1Subjects (10).409*** 2Vowel.412*** 3Voicing.034*** 4Place (2).050***

44. Regression F2 StepVariablesR 2 change P 1Subjects (10).409*** 2Vowel.412*** 3Voicing.034*** 4Place (2).050*** 5Vowel x Voicing.008*** 6Vowel x Place (2).015*** 7Voicing x Place (2).004***

45. Regression F2 StepVariablesR 2 change P 1Subjects (10).409*** 2Vowel.412*** 3Voicing.034*** 4Place (2).050*** 5Vowel x Voicing.008*** 6Vowel x Place (2).015*** 7Voicing x Place (2).004*** 8ContextVl (3).008*** 9ContextVL interactions (12).001- Total R 2 =.940 Post-hoc analysis: height + backness.

46. Regression Summary Progressively accounting for variance is powerful F1: 88% of variance F2: 94% of variance using only known sources of variance V-to-V coarticulation is readily apparent when other sources of variance are explained. How useful would this be? Effect of V-to-V coarticulation has a similar size to place/voicing effects.

47. Predicting Vowel Identity Multinomial Logistic Regression (MLR) Classification algorithm Predict category membership from multiple variables. Categories do not have to be binary Same i ɑ æ Context Vowel

48. Predicting Vowel Identity Assumes optimal listener. Computes % correct. How much well could a listener do under ideal circumstances with information provided. Multinomial Logistic Regression (MLR) Classification algorithm Predict category membership from multiple variables. Categories do not have to be binary

49. Predicting Vowel Identity 0 10 20 30 40 50 60 i ɑ æSame Vowel % Correct Partialed out Subject Vowel Place Voicing Interactions Model does quite well at predicting all vowels but the identity.

50. Predicting Vowel Identity -12 -10 -8 -6 -4 -2 0 2 4 6 -42814202632 F2 (Z) F1 (Z) ʌ-iʌ-i ʌ-æʌ-æ ʌ-ɑʌ-ɑ i æ ɑ -10 -8 -6 -4 -2 0 2 4 6 -18-12-6061218 F2 (Z) F1 (Z) ɛ-iɛ-i ɛ-æɛ-æ ɛ-ɑɛ-ɑ i æ ɑ

51. Predicting Vowel Identity Does partialing out other sources of variance improve the utility of V-to-V coarticulation? - Use linear regression to partial out variance. - Use F1, F2 residuals to predict vowels. FULL: Partial out everything RAW: No parsing SPEAKER:Partial out speaker variation only. Assume speaker normalization, but no interactions between consonant, or vowel and V-to V. VOWEL:Partial out effects of everything heard at the target vowel (speaker + target) NO-SPKR:Assume no normalization, but interactions between consonants.

52. Predicting Vowel Identity FULL: about 4% better than others. VOWEL: parsing out consonant may not be necessary SPEAKER: Effect of speaker and phonetic cues similar. RAW: V-to-V not useful without some parsing. 25 27 29 31 33 35 37 39 41 43 45 FULLVOWELSPEAKERNO-SPKRRAW % Correct

53. Predicting Vowel Identity 2) Regressively compensate for consonant coarticulation target vowel consonant context vowel preceding context 3) Use residuals to predict context vowel 1) Parse out speaker effects on target Suggests a 3-stage parsing process to maximally use V-to-V modifications.

54. Key Questions Extent of phenomenon Word boundaries? Both F1 and F2? Relative strength of V-to-V effects? Usefulness of phenomenon Perceptual inferences? Parsing our variability?

55. Summary: Extent Clear evidence for V-to-V coarticulation across word boundaries—not lexicalized. V-to-V in both formants (height + backness). Strength is similar to that of place and voicing. Known sources of variance (speaker, vowel, consonant, V-to-V) can account for most of the variability in vowel production. -Problem of lack of invariance? -Identifying multiple categories at once may be easier than identifying one.

56. Summary: Usefulness Idealized listener (+ parsing) could identify upcoming vowel at 40% correct given only V-to-V coarticulation. - Near 50% for /i/ and / ɑ / Parsing dramatically improves predictive power of V-to-V coarticulation Do you need perfect categorization of variance sources (e.g. speaker, target vowel, voicing…)? -Imperfect categorization enhances need for multiple cues. -Simultaneously evaluating multiple features (e.g. V1, C, V2) yields correct parse. How do you determine the order of parsing? - Temporal order of information arrival?

57. Future Directions How do you identify the components you will be parsing? See Toscano poster. Does the model actually describe perception? Parsing is a temporal process. Visual world paradigm to time-course of processing (e.g. McMurray, Clayards, Tanenhaus, in prep; McMurray, Tanenhaus & Aslin, 2002; McMurray, Munson & Gow, submitted). Parsing as part of word recognition. Lexical structure can contribute to inferences. Interactive activation models (McClelland & Elman, 1986) could implement this.

58. Conclusions Where do features come from? Emerge out of progressively accounting for sources of variance from signal. Any “chunk” (segment) of the input can provide multiple features. Speaker normalization may work by same process. Why phonologize? Eliminates one step of parsing. How does the system balance need for features with utility of fine-grained detail? Features provide tag to parse variance and utilize continuous detail.