Three examples of sound-system research using web-available materials Andy Wedel LSA Summer...
-
Upload
alexis-sullivan -
Category
Documents
-
view
218 -
download
1
Transcript of Three examples of sound-system research using web-available materials Andy Wedel LSA Summer...
Three examples of sound-system research
using web-available materials
Andy Wedel
LSA Summer Institute: The Data Goldmine
July 9, 2015
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
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
1. Functional load and diachronic phoneme inventory change
With: Abby KaplanDepartment of Linguistics
University of Utah
Scott JacksonCenter for the Advanced Study
of Language (CASL)
University of Maryland
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
– 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?
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
Functional load
Specific Hypothesis: Neutralization is less likely for contrasts
that have a higher functional load. (Martinet 1955, Hockett 1967)
9
Phoneme Mergers
/ ɑ ~ ɔ / merger in western American English
cot
ɑ
ɔ caught
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)
Why hadn’t this been successfully tested before?
• Previous approaches involve case-studies:1. Find a contrast merger or set of mergers2. Assess the change in the system given your favorite
measure of functional load3. 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
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
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.
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?
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
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
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
• German• Dutch, RP English • American English• Spanish• French• Turkish• Korean• HK Cantonese
Find word lists from a variety of languages
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
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– ArisoyTurkishData– LemmaForms
American English: moderately easy to work with
• Get standard US pronunciation from Carnegie-Mellon Pronouncing Dictionary (CMUDict)
• Frequency databases freely available– CELEX, SubtlexUS– How to deal with homographs?
• Example output files with ND calculated– LemmaForms
Spanish: More complex
• Spanish Gigaword corpus (Linguistics Data Consortium)– Text files from newswires– Example
• Use TreeTagger to morphologically parse and add categories
• Example of output
• Map to phonemic representation and count• Show code and output
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.
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
56 mergers524 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.
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
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
Independent measures
• Sublexical measures:– Phoneme type/token frequencies
• uniphone, biphone, triphone
– Sublexical entropy change upon merger– Dataset example
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
Number of minimal pairs is inversely correlated with merger
32Wedel, A., A. Kaplan & S. Jackson (2013). Language and Speech.
What kind of minimal pairs?
Lemma vs word form?
Within vs Between Category?
Frequency?
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
Intriguing: Higher phoneme frequency is positively correlated with merger
…but only for phoneme pairs that don’t distinguish minimal pairs.
Example model predictions
36
American English
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.
Phoneme Shifts
- California Vowel shift
fat
u
ɑæ
oɛ
ɪ
dude
dress
Phoneme Splits
– Vowel length split in Pittsburgh English• town ~ ton
taʊn ~ tʌn tʌ:n ~ tʌn
a
ʌ townton
ʊ
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.
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
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
Mergers versus Shifts and Splits
phoneme mergers
phoneme splits/shifts
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
Given a change, predicting its type
log minimal lemma pair count
MergerShift/Split
Individual datasets
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.
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.
2. Lexical competition and hyperarticulation in natural speech
With: Becky SharpDepartment of Linguistics
University of Arizona
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
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.
Change in distribution of word variants
Change in distribution of sublexical variantsacross 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:
ArticulationPerceptionCognitive biasesSocial factorsSystem-internal patternsAcquisition biases…
52
Bias toward accurate transmissionof lexical information
Biases onphonetic formof word tokens
[Within-phoneme category variants]
Selection for word-level contrast
/Phoneme category evolution/
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
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)
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.
Dispersion as the outcome of competition makes the wrong prediction for vowel system change: Vowels can centralize in chain-shifts.
American Northern Cities Shift
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
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
Use the Buckeye Corpus of Conversational Speech
• 40 one-hour sociolinguistic interviews• gender and age balanced• obtained in Columbus, Ohio in 2000• Densely annotated:
– Phonemic transcription– Phonetic transcription– Syntactic category– Textfiles, phonefiles
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
Stop length, burst and offset
62A pea A bee
burst
length
[p] [b]
VOT data creation
• Annotate stop beginning, burst and offset using Praat.– Get lots of undergraduate helpers for this…
Praat example
Get dependent measure
• Script that processes Praat textgrid textfile to obtain:– Stop length, burst length– Use burst/length ratio as a rate-normalized
measure of VOT (Yao 2007)
Get independent factors of interest
• Minimal pair existence– Carnegie Mellon Pronouncing Dictionary
• Neighborhood density– Calculate independently– IPhOD
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
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
Relationship of Neighborhood Density to Burst/length Ratio
Lexical Neighbors
Bur
st/le
ngth
rat
io
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)
Get control factors
• From Buckeye word files:– Word identity
• previous, target, and following word
– Word category– Previous Mention– Speech rate
• syllables per second in local utterance
Get control factors
• From corpora, get forward/backward bigram probability– Google n-gram– Fisher English Training set
• see Seyfarth 2014 for example
Get control factors
• From IPhOD:– SubtlexUS-based word frequency– Neighborhood density– Positional two-segment probability averaged
over the word (Vitevitch & Luce 2004)
Voiceless Stop Model
Voiced Stop Model
Can we find this effect in vowels? Measuring vowel-vowel distances
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’.
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
Measuring from [i]
iɪ
eɛ
æʌ
Measuring from [ɛ]
iɪ
e
ɛ
æʌ
Minimal pair existsMinimal pair does not exist
more distinctive
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
Measuring from [ɛ]
iɪ
eɛ
æʌ
LME modelmodel = lmer (EuclideanDistance ~
MinimalPair+
Neighborhood+
Alternative +
VowelLength +
Vowel_CompetitorVowel +
(1+ MinimalPair+Neighborhood+Alternative+VowelLength|Speaker) + (1|Lemma), data = k, REML = F)
Model output
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.
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.