1. Three examples of sound-system research using web-available materials Andy Wedel LSA Summer Institute: The Data Goldmine July 9, 2015

2. 1.Functional load and diachronic phoneme inventory change –Published literature on sound-change in combination with phonemically-coded corpora 2.Lexical competition and hyperarticulation in natural speech –Phonetic measures in the Buckeye Corpus in combination with lexical data on English 3.Correlation between crosslinguistic and language-internal phoneme frequencies –A database of phoneme inventories combined with available phonemically-coded corpora

3. Organizational steps in research What is the question? –Identify your general hypothesis What is the approach? –Operationalize your hypothesis –Develop a method/experiment Find data/create materials Analysis/Results Dissemination

4. 1. Functional load and diachronic phoneme inventory change With: Abby Kaplan Department of Linguistics University of Utah Scott Jackson Center for the Advanced Study of Language (CASL) University of Maryland

5. Functional load and diachronic phoneme inventory change 1.What is the question? 2.What is the approach? 3.Find data/create materials 4.Analysis/Results

6. Phoneme inventories change over time 6

7. –Gilliéron (1918), Jakobson (1931), Mathesius (1931), Trubetzkoy (1939) –Martinet (1952), King (1967), Hockett (1967) –Surendran & Niyogi (2006), Silverman (2011), Kaplan (2011) Does the functional load of a phoneme contrast influence its trajectory of change?

8. Functional load “The notion of functional load is that a phonemic system … has a (quantifiable) job to do, and that the contrast between any two phonemes, say /a/ and /b/, carries its share.” Charles Hockett 1967 8

9. Functional load Specific Hypothesis:  Neutralization is less likely for contrasts that have a higher functional load. (Martinet 1955, Hockett 1967) 9

10. Phoneme Mergers / ɑ ~ ɔ / merger in western American English cot ɑ ɔ caught

11. How has functional load been operationalized? In terms of the lexicon: –Number of minimal pairs (Martinet 1955) Various ways of counting number of homophones (Silverman 2009, Kaplan in press) –Lexical level entropy (Surendran and Niyogi 2006) In terms of the sound system –Type or token phoneme frequency (Currie-Hall 2010) –Phoneme level entropy (Hockett 1967, King 1967, Surendran and Niyogi 2006)

12. Why hadn’t this been successfully tested before? Previous approaches involve case-studies: 1.Find a contrast merger or set of mergers 2.Assess the change in the system given your favorite measure of functional load 3.Compare to a set of similar contrasts that did not merge. 4.Is the change in the system smaller for the actual mergers than for the non-mergers? Problem: if we assume that functional load is just one of many factors influencing sound change, we expect many ‘exceptions’ to the hypothesis.  We need to assess outcomes statistically. 12

13. Functional load and diachronic phoneme inventory change 1.What is the question? 2.What is the approach? 3.Find data/create materials 4.Analysis/Results

14. Strategy for dealing with data sparseness, diversity of data source 1.Pool data on mergers from multiple languages. 2.Use linear mixed effects modeling. –Random effects structure helps control for structure inherent in different data-sources.

15. What’s the balance between hypothesis generation and testing? Broad general hypothesis to be tested: –Functional load predicts merger Narrower hypotheses to be explored: –what specific measure(s) of functional load are predictive?

16. Functional load and diachronic phoneme inventory change 1.What is the question? 2.What is the approach? 3.Find data/create materials 4.Analysis/Results

17. Building a database Hockett 1955 “... unfortunately the determination [of functional load] has not been made yet [because] the amount of counting and computation is formidable, so we can give no example...”  Use existing frequency corpora to build a large database of reasonably recent mergers and associated comparison sets. 17

18. Find word lists from a variety of languages We don’t know what measure of functional load is appropriate: want to be able to test a variety of measures –Minimal pair count –Average neighborhood density –System entropy Requirements for each word list: –Phonemically coded –Lemmatized –Frequency

19. German Dutch, RP English American English Spanish French Turkish Korean HK Cantonese Find word lists from a variety of languages

20. Material won’t perfectly match your question Key! –Always keep your eyes open for new data sources. –Be ready to do some work to transform information into a form appropriate for your question. –You’ll often have to make semi-arbitrary decisions Keep notes, and be ready to describe/defend your choices. Examples differing in ease: –Turkish > American English > Spanish

21. Turkish: easy to work with Obtained by emailing authors Easy to work with: –Orthographic coding already near-phonemic coding is pre-merger –Morphologically parsed into stem + affixes –Syntactic category given –ArisoyTurkishDataArisoyTurkishData –LemmaFormsLemmaForms

22. American English: moderately easy to work with Get standard US pronunciation from Carnegie-Mellon Pronouncing Dictionary (CMUDict) (CMUDict) Frequency databases freely available –CELEX, SubtlexUSCELEX –How to deal with homographs? Example output files with ND calculated –LemmaFormsLemmaForms

23. Spanish: More complex Spanish Gigaword corpus (Linguistics Data Consortium) –Text files from newswires –ExampleExample Use TreeTagger to morphologically parse and add categoriesTreeTagger Example of output Map to phonemic representation and count Show code and outputcodeoutput

24. Looking for changes of interest Look through the literature for diachronically recent phoneme mergers in varieties of these languages that share the same phonemic inventory as the dialect on which the word list is based. –For example: American and RP English have distinct vowel inventories; RP and Australian English share phoneme inventories, even though they are phonetically different.

25. Looking for changes of interest Identify a set of comparison phonemes of the same major class (consonant, vowel) as the merged phoneme pair that are phonologically similar. –1 basic feature distant, e.g., t ~ d, t ~ k, u ~ o

26. 56 mergers 524 non-mergers 8 languages 26 18 phoneme-pair systems: Each contains at least one merger, and as comparisons, all other phoneme pairs in the same major class (vowel or consonant) that are one phonological feature apart. Wedel, A., A. Kaplan & S. Jackson (2013). Language and Speech. Wedel, A., S. Jackson A. Kaplan (2013). Cognition.

27. Independent measures Lexical measures: –Number of minimal pairs distinguished by each phoneme pair Write a script that goes through each phonemic form, merges the contrast using a regular expression, and counts how many other phonemic forms it becomes identical to. –Lemma vs word-form counts –Within/across word category

28. Independent measures Lexical measures: –Number of lexical ‘prefixes’ distinguished by each phoneme pair (Cohen-Priva, in press) –Average neighborhood density for words containing each phoneme –Lexical entropy change on merger (Surendran & Niyogi 2006)

29. Calculating functional load in terms of informational entropy (Shannon 1951) General form (Hockett 1967, Surendran and Niyogi 2006) : FL(a ↔ b) = H(L) − H(La↔b) H(L) where 29

30. Independent measures Sublexical measures: –Phoneme type/token frequencies uniphone, biphone, triphone –Sublexical entropy change upon merger –Dataset exampleDataset example

31. Functional load and diachronic phoneme inventory change 1.What is the question? 2.What is the approach? 3.Find data/create materials 4.Analysis/Results

32. Number of minimal pairs is inversely correlated with merger 32 Wedel, A., A. Kaplan & S. Jackson (2013). Language and Speech.

33. What kind of minimal pairs? Lemma vs word form? Within vs Between Category? Frequency?

34. What does not seem to substitute for minimal pairs in this effect? Lexical measures –Neighborhood measures –Lexical entropy change Sublexical measures –sublexical entropy changes –uniphone, biphone, triphone probabilities

35. Intriguing: Higher phoneme frequency is positively correlated with merger …but only for phoneme pairs that don’t distinguish minimal pairs.

36. Example model predictions 36 American English

37. What about changes that might index avoidance of merger? Phoneme Shift: concerted shift of a phoneme pair in the same dimensional space. Phoneme Split: merger of a contrast associated with enhancement of an associated contrast in a different dimension.

38. Phoneme Shifts - California Vowel shift fat u ɑ æ o ɛ ɪ dude dress

39. Phoneme Splits –Vowel length split in Pittsburgh English town ~ ton ta ʊ n ~ t ʌ n  t ʌ :n ~ t ʌ n a ʌ town ton ʊ

40. What’s the balance between hypothesis generation and testing? We already have a strong prediction that a small number of within-category minimal lemma pairs predicts merger. Narrower hypothesis to be explored: –Shifts and splits… which are phoneme inventory changes that preserve lexical distinctions… – are correlated with a significantly larger number within-category minimal lemma pairs.

41. Get examples of shifts/splits in our set of languages Shifts –Spanish voiced/voiceless stop pairs Lewis 2000 –American English vowel shifts: Northern cities, Southern Shift Labov et al. 2006 –NZ English front vowel shifts Hay, Macglagan, & Gordon 2008 –Polder Dutch diphthongs Jacobi 2009 –Canadian French vowel shift Walker 1983

42. Database of Shifts/Splits Splits –Pittsburgh / ɑʊ ~ ʌ /, Inland North /e ~ ɑ /  vowel length Labov et al, 2006 –Turkish ɣ deletion  vowel length Lewis 1967 –NZE /dress ~ fleece/  diphthongization Maclagan and Hay, 2005 –Korean onsets /lax ~ aspirated/  tone Silva 2006

43. Mergers versus Shifts and Splits phoneme mergers phoneme splits/shifts

44. Can we predict the direction of change? Given a phoneme-inventory change, was it –a change that reduces lexical distinctions?  a merger –a change that preserves lexical distinctions?  a shift or a split

45. Given a change, predicting its type log minimal lemma pair count Merger Shift/Split

46. Individual datasets

47. New insights The distribution of a phonological contrast across the lexicon influences the trajectory of change in that phonological contrast. Results in maintenance of a compact phoneme inventory. –Contrasts that support few lexical contrasts tend to be lost. –Contrasts that support more lexical contrasts are preserved, or provide seed variation for new contrasts.

48. Take home message with respect to big data and computation… New data sources, models and technologies allow us to better test hypotheses concerning the relationship of the form of sound systems to their function in communication.

49. 2. Lexical competition and hyperarticulation in natural speech With: Becky Sharp Department of Linguistics University of Arizona

50. Lexical competition and hyperarticulation in natural speech 1.What is the question? 2.What is the approach? 3.Find data/create materials 4.Analysis/Results

51. Big question If the existence of minimal pairs influences change in a phoneme contrast, what are the mechanisms, at various levels? Theoretical Prediction: (e.g., Lindblom 1990, Wright 2004, Wedel 2012…)  Phonetic cues that support communication are hyperarticulated in usage.  Consistent usage biases drive phonological change and pattern formation.

52. Change in distribution of word variants Change in distribution of sublexical variants across the lexicon e.g., Baudouin de Courtenay 1895, Ohala 1989, Lindblom 1990, Bybee 2001, Blevins 2004, Baese-Berk & Goldrick 2009, Ernestus 2011, Wedel et al. in press Wang 1969, Bybee 2002, Phillips 2006, Kraljic & Samuel 2009, Hay and Maclagan 2012 Theoretical/Linguistic/Experimental evidence: Articulation Perception Cognitive biases Social factors System-internal patterns Acquisition biases … 52 Bias toward accurate transmission of lexical information Bias toward accurate transmission of lexical information Biases on phonetic form of word tokens

53. [Within-phoneme category variants] Selection for word-level contrast /Phoneme category evolution/

54. Lexical competition and hyperarticulation in natural speech 1.What is the question? 2.What is the approach? 3.Find data/create materials 4.Analysis/Results

55. Background/Previous Work Previous work done using lab speech Small effects, fragile results –VOT is slightly hyperarticulated for initial stops given minimal pairs in list reading (Baese & Goldrick 2009, Peramunage et al. 2011). –VOT hyperarticulated on first production of words with a visual stop-competitor in the context (Kirov & Wilson 2012) –In a lab-speech paradigm designed to elicit hyperarticulation away from a vowel competitor, tense/lax vowel duration differences increased, but not formant differences (Schertz 2015)

56. Work with vowels has focused on ND as the trigger for hyperarticulation, and dispersion as the outcome Dispersion = distance of a vowel in F1-F2 space from the center of the vowel space But: vowel change patterns suggest that competition-driven hyperarticulation should be more phonetically specific. –Correlation of minimal pair count with vowel shift patterns link competition to shifts –Vowel chain shifts often involve moves toward the center of the vowel space.

57. Dispersion as the outcome of competition makes the wrong prediction for vowel system change: Vowels can centralize in chain-shifts. American Northern Cities Shift

58. Ok, so how to approach this? 1.Use natural speech instead of lab speech 2.Compare minimal pair existence to neighborhood density as a predictor for hyperarticulation 3.Look at both VOT for stops and F1-F2 Euclidean distance for vowels –For vowels, compare F1-F2 distance to dispersion

59. Lexical competition and hyperarticulation in natural speech 1.What is the question? 2.What is the approach? 3.Find data/create materials 4.Analysis/Results

60. Use the Buckeye Corpus of Conversational SpeechBuckeye Corpus 40 one-hour sociolinguistic interviews gender and age balanced obtained in Columbus, Ohio in 2000 Densely annotated: –Phonemic transcription –Phonetic transcription –Syntactic category –Textfiles, phonefilesTextfilesphonefiles

61. VOT in word-initial stops Use a perlscript to identify appropriate material in the Buckeye Corpus –words starting with [ptkbdg] –content words –1, 2 syllables –no preceding/following utterance or disfluency boundary –no preceding word-final stop Measure closure and burst lengths

62. Stop length, burst and offset 62 A peaA bee burst length [p] [b]

63. VOT data creation Annotate stop beginning, burst and offset using Praat. –Get lots of undergraduate helpers for this…

64. Praat example

65. Get dependent measure Script that processes Praat textgrid textfile to obtain:Praat textgrid textfile –Stop length, burst length –Use burst/length ratio as a rate-normalized measure of VOT (Yao 2007)

66. Get independent factors of interest Minimal pair existence –Carnegie Mellon Pronouncing Dictionary Neighborhood density –Calculate independently –IPhODIPhOD

67. Lexical competition and hyperarticulation in natural speech 1.What is the question? 2.What is the approach? 3.Find data/create materials 4.Analysis/Results

68. Voiced Stops Voiceless Stops No MinPairs MinPairs No MinPairs MinPairs Burst/length ratio by minimal pair existence: Initial stops distinct in voice bat pat badge pant

69. Relationship of Neighborhood Density to Burst/length Ratio Lexical Neighbors Burst/length ratio

70. Factors in Linear Mixed Effects modeling Stop-voicing minimal pair competitor existence Neighborhood density Control factors: –local speech rate –word category –forward/backward bigram probabilities –word frequency –previous mention –syllable number –stop identity –following high (liquids, rhotics, high vowels)

71. Get control factors From Buckeye word files:word files –Word identity previous, target, and following word –Word category –Previous Mention –Speech rate syllables per second in local utterance

72. Get control factors From corpora, get forward/backward bigram probability –Google n-gram –Fisher English Training setFisher English Training set see Seyfarth 2014 for example

73. Get control factors From IPhOD:IPhOD –SubtlexUS-based word frequency –Neighborhood density –Positional two-segment probability averaged over the word (Vitevitch & Luce 2004)

74. Voiceless Stop Model

75. Voiced Stop Model

76. Can we find this effect in vowels? Measuring vowel-vowel distances

77. Vowel distances in initial syllables Identify material in the Buckeye corpus of Conversational English –words with an initial syllable non-back monophthong –content words –1 syllable –no preceding/following utterance or disfluency boundary –no words with ablaut in their paradigm e.g., no ‘sit’, because of ‘sat’.

78. Dataset construction For each word token, measure vowel distance to three neighboring vowels. Starting dataset has three measures per word token: Split randomly into three datasets with one measure per token. Randomly choose one dataset for statistical analysis. Minimal Pair existence

79. Measuring from [i] i ɪ e ɛ æ ʌ

80. Measuring from [ ɛ ] i ɪ e ɛ æ ʌ

81. Minimal pair exists Minimal pair does not exist  more distinctive 

82. Factors Vowel-vowel minimal pair competitor existence Neighborhood density Vowel-vowel minimal pair competitor existence in one of the other two neighboring vowels Control factors: –local speech rate –forward/backward bigram probabilities –word frequency –previous mention –vowel length –vowel-vowel pair identity

83. Measuring from [ ɛ ] i ɪ e ɛ æ ʌ

84. LME model model = lmer (EuclideanDistance ~ MinimalPair+ Neighborhood+ Alternative + VowelLength + Vowel_CompetitorVowel + (1+ MinimalPair+Neighborhood+Alternative+VowelLen gth|Speaker) + (1|Lemma), data = k, REML = F)

85. Model output

86. What about dispersion? Run the same kind of analysis using vowel-center distance. Factors that significantly predict dispersion: –Word Frequency –Vowel Length Neighborhood density and minimal pair competitor existence are not predictive.

87. Summary Phonetic cues that contribute strongly to distinguishing words tend to be hyperarticulated in natural speech –VOT in initial stops –F1-F2 distance in vowels Consistent with idea that phoneme contrast is maintained in part by a bias toward lexical contrast.  maintains an efficient set of phoneme contrasts over language change: phonemes that do not distinguish many words are vulnerable to loss.