Linguistic Structure in Identifying Segments in a Second Language

1. Linguistic Structure in Identifying Segments in a Second Language Kenneth de Jong Indiana University Colloquium at the Department of Linguistics THE ohio state university May 6, 2005

2. Also with help & collaboration from NIH in R03 DC04095 & NSF in BC-9910701 Kyoko Nagao Byung-jin Lim Hanyong Park Noah Silbert Minoru Fukuda of Miyazaki Municiple University Jin-young Tak of Sejong University Mi-hui Cho of Kyonggi University

3. Second Language Phonetic and Phonological Research • Very popular topic • … especially lately • … relative to a lot of what we linguists do • Very large literature • Very unsatisfying - difficult to really gain a coherent picture of what’s really known about the field

4. 2 Reasons for the Nature of Literature • Segregated research threads - different questions, different data-treatment approaches • Classroom oriented research in such groups as TESOL • Generative-style formal analyses done by linguists • Cross-language perception studies by psychologists • Motor learning studies done by (almost) no one • It’s hard • Requires double the linguistic expertise, since we deal with two linguistic systems • Requires a set of typological comparisons that will support a model of how the two systems map onto one another • Requires sufficient detail in all of the above to make the models reasonable

5. So … why do it? • We have a hard time saying no for the 16th time • Most people are multi-lingual, esp. today • Theoretically useful • The level of rigor, specificially with respect to typological claims, is useful for the discipline • Rapid learning in the second language acquirer can show us how the linguistic cognitive system works

6. Topic today:Segmental Identification • Predominates phonological and psychological literature • Relatively simple (given what we know) • Largely abstracted from lexical access and syntactic parsing issues (until recently) • Essentially alphabetic

7. Previous, very very commonly cited models • SLM - Jim Flege: • Model of production (originally) • Production problems depend on perceptual meta-classification of segments, where segments = allophone (more or less) • New v.s Similar = whether a segment in L2 has a corresponding segment in L1 • Beats me how we know what counts as similar, but I’m sure the IPA has something to do with it • In early learning, similar phones are stable and functional, while new phones are unstable and dysfunctional • Learning of new phones progresses rapidly, while similar phones merge with L1 phones to form a stable and not-quite-accurate category

8. Previous, very very commonly cited models • PAM - Cathy Best: • Model of perceptual discrimination • Discrimination abilities depend on perceptual meta-classification of segments, where segments = gestural complexes (more or less) • The degree to which two contrasting sounds fit into different categories, given L1 experience, determines the degree to which they can be discriminable by an L1 perceiver • Not a model of second language learning, but of cross-language perception; technically subjects should be set free after the experiment, since the experiment breaks them by beginning the process of forming additional perceptual categories

9. Model Architecture: Segmental Categories are Unitary Things • Most of these experimentally oriented models treat segments as unitary free-standing object categories • At odds with typical treatment in linguistic models which generally assume that cross-segment properties are operative in determining how second language classification happens

10. Model Architecture: Segmental Categories are Unitary Things Questions to be pursued • Parsing question: Segments are embedded Iarger units of all different kinds • Cross-segment question: Segments exist in a matrix with other segments • Within-segment question: Segments have lots of internal structure

11. Model Architecture: Segmental Categories are Unitary Things Questions to be pursued • Parsing question: Segments are embedded Iarger units of all different kinds • Cross-segment question: Segments exist in a matrix with other segments • Within-segment question: Segments have lots of internal structure

12. Parsing Question • Analyses of Korean -> English database for other studies below • Park & de Jong (2005) shows that prosodic parsing heavily affects segmental identification • C’s in VC’s are neutralized, but C’s in VCV’s are not • Korean listeners’ accuracy voicing judgments for word-final obstruents depend on whether they hear a count a VC release as an additional syllable

13. Model Architecture: Segmental Categories are Unitary Things Questions to be pursued • Parsing question: Segments are embedded Iarger units of all different kinds • Cross-segment question: Segments exist in a matrix with other segments • Within-segment question: Segments have lots of internal structure

14. Experiment:Cross-segment question • Corpus • English obstruents with /a/ to make non-words • 8 Target consonants contrasting in three binary features Coronal Labial Voiced Voiceless Voiced Voiceless Stops /d/ /t/ /b/ /p/ Fricatives // // /v/ /f/ • 4 Prosodic conditions Intervocalic At Edge Pre-stress /∂ ‘pa/ ‘apah’ /pa/ ‘pa’ Post-stress /’a p∂/ ‘oppa’ /ap/ ‘op’ • Analysis: Look for generality across parallel segments

15. Experiment:Cross-segment question • Stimuli • 4 Northern mid-western English speakers in late 20’s • Cued with orthographic fonts • One consonant per non-word item, consonant included others besides the 8 targets • Produced in isolation • Listeners • 41 Korean undergrads at Kyonggi University in Seoul • Very little exposure to native English-speaking people • Procedure • Stimuli presented over headphones in a listening lab • Listeners asked to identify the consonants on a paper response sheet • Given 14 response options + one (rarely used) for ‘other:____’

16. Analysis for Generalization 1: Cross-listener differences • Question: Is segmental accuracy with one segment tied to accuracy with parallel segments • Here: contrasting non-sibilant fricatives are new for the Korean listeners. They need to be distinguished from stops which are similar. (C.f. looking for copy machines in the kitchen.) • Specific sub-question: is accuracy in distinguishing /t/ from // linked to accuracy in distinguishing /p/ from /f/? • Regress accuracy for each listener in coronals against accuracy in labials

17. Manner accuracy: Labials vs. Coronals • Error rates range from 50% to 10% • Accuracy often better with coronals • The two accuracy scores do correlate quite strongly • But … what about, say, voiced and voiceless, where the contrast is quite different?

18. Manner accuracy: Voiced vs. Voiceless • Accuracy difference is larger. • Voiced obstruents are poorly distinguished, never less than 20% error rates • BUT again: the two accuracy scores do correlate • Next: split by prosodic position

19. Manner accuracy: Across prosodic positions • Correlations generally in the same ball-park as we just saw, with exception of Final position • Even here, the correlations are strongly significant

20. Interim Summary • Results suggest that distinguishing stops from fricative is a single skill (or at least a set of closely related skills). Some listeners have acquired it better than others. • Woah. Um … how do we know this isn’t just an effect of overall proficiency differences. Some listeners are more experienced, and hence are better categorizers overall? • Good question. • However, the correlation patterns for the manner contrasts are not obtained for all pairs. C.f., the voicing contrast below.

21. Voicing accuracy: Across prosodic positions • Correlations only between • Initial (‘pa’) and pre-stress (‘apah’) • Pre-stress (‘apah’) and post-stress (‘oppa’) • Suggests three skills: pre-vocalic, inter-vocalic, and post-vocalic

22. Analysis for Generalization 2:Part-whole Analysis • Boothroyd & Nittrouwer (1988) point out mathematical difference between unitary and generalized, factored models • Factored models predict that the accuracy of the whole is the product of accuracy in each of the factors • Here, e.g., accuracy in identifying /f/ = accuracy in manner X accuracy in voicing X accuracy in place • ‘J-factor’: segment accuracy = (average feature accuracy)J • With a factored model, we expect J = number of factors, here 3 • With a largely unitary model, we expect J < 2 (or so, Nearey, 2003) • Benki (2003) also finds familiarity biasing in which more familiar items exhibit lower J-factors (between 2 & 3 in his study)

23. Part-whole Analysis • J-factors split by prosodic position • J-factors consistently near 3 • Lowest J-factors in initial position - familiarity biasing effect? • Do similar analyses of different segments

24. Part-whole Analysis • Segmental accuracy is very close to the product of featural accuracies for each segment • Fricatives lie almost exactly on diagonal • Stops are often slightly over diagonal • Since Korean has stops, this suggests a familiarity biasing effect

25. Summary • Evidence against a strictly segmental model of segment identification • Cross subject correlations have parallelism in accuracy rates which is parallel to the featural structure of the consonants being acquired • Evidence for a generalized model • Overall accuracy in segmental identification is neatly a function of accuracy in the component features. This is particularly true for novel segments being acquired • Related evidence below

26. Model Architecture: Segmental Categories are Unitary Things Questions to be pursued • Parsing question: Segments are embedded Iarger units of all different kinds • Cross-segment question: Segments exist in a matrix with other segments • Within-segment question: Segments have lots of internal structure

27. Experiment:Internal structure question • Corpus • 4 Midwestern American speakers in their mid-30’s • /pi/ and /bi/ • Metronomically Rate-varied corpus with extreme durational variability (deJong, 2001a; 2001b) • Repetition period varied continuously from 450 ms - 250 ms • This range of rates from physiological constraints study (Nelson & Perkell, 19**) • Procedure • Present excised syllable trains for identification • Subjects • 23 native English speaking undergraduates from Indiana University • 14 native Japanese speaking students from Indiana University • 13 native Korean speaking students from Indiana University • All monolingual through early years

28. Stimulus VOT Distribution • Plots VOT for /p/ and /b/ against syllable duration • VOT’s shorten for /p/ at fast rates

29. Stimulus VOT Distribution • Zoom in on VOT dimension • Get near merger at very fast rates

30. Native Responses • Logistical regression with identification responses • Add 50% boundary between /p/ & /b/ for native listeners • Slant shows normalization for rate

31. Question: how do Non-natives handle variability? • Mismatch in VOT production boundary • Japanese /p/ has shorter VOT • Korean /ph/ has longer VOT • Expect shifted identification responses • Japanese: more /b/ -> /p/ errors • Korean : more /p/ -> /b/ errors

32. Cross-language • Get shifts in expected directions • Rate normalization function is same as native listeners

33. Question: how do Non-natives handle variability? • Get expected shifted identification responses • Japanese: more /b/ -> /p/ errors • Korean : more /p/ -> /b/ errors • Rate normalized as well. • Question is: where? • Segmental Un-rate-differentiated Prototype: mostly in middle of distribution • Rate Extracted Model: persistent across distribution

34. Undifferentiated Prototype Model • Here’s the general distributional pattern

35. Undifferentiated Prototype Model • Here are prototypical categories with centers to which stimuli are compared

36. Undifferentiated Prototype Model • Using native vs. non-native centers heavily affects portions between the centers • Distance of extreme tokens from two centers is little affected

37. Extracted Model • A generalized criterion model divides space

38. Generalized Model • A shifted criterion will affect identification throughout region around boundary

39. Non-native Differences • Back to Actual responses • We compare native and non-native identification and highlight tokens which differ

40. Japanese Differences • Expect /b/->/p/ errors • Get more (red squares) • Note distribution across rates • Also get /p/ -> /b/ errors (black diamonds)

41. Korean Differences • Expect /p/->/b/ errors • Get them (black diamonds) • Note very odd distribution: across rates? • Also get /p/ -> /b/ errors (red squares)

42. Experiment 2 Summary • Differences in L1 typical VOT show up in mismatch errors in both Japanese and Korean • Errors are distributed across the rates, suggesting a model in which generalized perceptual criteria are taken from L1 • Reverse direction errors also indicate another aspect of non-native boundaries: Uncertainty

43. Model Architecture: Segmental Categories are Extracted Things Questions to be pursued • Parsing question: Segmental identification requires global identification of context • Cross-segment question: Segmental identification is a function of other segments • Within-segment question: Segmental identification is a function of generalized situation

44. Fine