An R package for phonological analysis

For a more comprehensive vignette, visit my website. The package requires R >= 4.1.

• getFeat() and getPhon() to work with distinctive features
• ipa() phonemically transcribes words (real or not) in Portuguese or Spanish
  • ipa_pt() offers a more detailed transcription for Portuguese
• sonDisp() calculates the sonority dispersion of a given demisyllable. meanSonDisp() calculates the average dispersion for a given word (or vector of words)
• wug_pt() generates hypothetical words in Portuguese
• biGram_pt() calculates bigram probabilities for a given word
• plotVowels() generates vowel trapezoids
• plotSon() plots the sonority profile of a given word
• psl contains the Portuguese Stress Lexicon
• pt_lex contains a simplified version of psl
• stopwords_pt and stopwords_sp contain stopwords in Portuguese and Spanish

The function getFeat() requires a set of phonemes ph and a language lg. It outputs the minimal matrix of distinctive features for ph given the phonemic inventory of lg. Five languages are supported: English, French, Italian, Portuguese, and Spanish. You can also use lg to provide your own phonemic inventory as a vector. Here are some examples.

library(Fonology)

getFeat(ph = c("i", "u"), lg = "English")
#> [1] " hi"    " tense"
getFeat(ph = c("i", "u"), lg = "French")
#> [1] "Not a natural class in this language."
getFeat(ph = c("i", "y", "u"), lg = "French")
#> [1] " syl" " hi"
getFeat(ph = c("p", "b"), lg = "Portuguese")
#> [1] "-son"  "-cont" " lab"
getFeat(ph = c("k", "g"), lg = "Italian")
#> [1] " cons" " back"

The function getPhon() requires a feature matrix ft and a language lg. It outputs the set of phonemes represented by ft given the phonemic inventory of lg. The languages supported are the same as those supported by getFeat(), and you can again use lg to provide your own phonemic inventory as a vector.

getPhon(ft = c(" syl", " hi"), lg = "French")
#> [1] "u" "i" "y"
getPhon(ft = c("-DR", "-cont", "-son"), lg = "English")
#> [1] "t" "d" "b" "k" "g" "p"
getPhon(ft = c("-son", " vce"), lg = "Spanish")
#> [1] "z" "d" "b" "ʝ" "g" "v"

The function ipa() takes a word (or a vector with multiple words, real or not) in Portuguese or Spanish in its orthographic form and returns its phonemic (i.e., broad) transcription. Narrow transcription is available for Portuguese (based on Brazilian Portuguese), which includes secondary stress—this can be generated by adding narrow = T to the function. Run ipa_pt_test() and ipa_sp_test() for sample words in both languages. By default, ipa() assumes that lg = "Portuguese" (or lg = "pt") and narrow = F.

ipa("atletico")
#> [1] "a.tle.ˈti.ko"
ipa("cantalo", narrow = T)
#> [1] "kãn.ˈta.lʊ"
ipa("antidepressivo", narrow = T)
#> [1] "ˌãn.t͡ʃi.ˌde.pɾe.ˈsi.vʊ"
ipa("feris") 
#> [1] "fe.ˈɾis"
ipa("mejorado", lg = "sp")
#> [1] "me.xo.ˈɾa.do"
ipa("nuevos", lg = "sp")
#> [1] "nu.ˈe.bos"

A more detailed function, ipa_pt(), is available for Portuguese only. In it, stress is assigned based on two scenarios. First, real words (non-verbs) have their stress assignment derived from the Portuguese Stress Lexicon (Garcia 2014)—if the word is listed there. Second, nonce words follow the general patterns of Portuguese stress as well as probabilistic tendencies shown in my work (Garcia, 2017a, 2017b, 2019). As a result, a nonce word may have antepenultimate stress under the right conditions based on lexical statistics in the language. Likewise, words with other so-called exceptional stress patterns are also generated probabilistically (e.g., LH] words with penultimate stress). Stress and weight are also used to apply both spondaic and dactylic lowering to narrow transcriptions, following work such as Wetzels (2007). Secondary stress is provided when narrow = T. In the function ipa(), stress is not probabilistic (and therefore not variable): it merely follows the orthography as well as the typical stress rules in Portuguese (and Spanish).

There are several assumptions about surface-forms when narrow = T (Portuguese only). Most of these assumptions can (and probably will) be adjusted as the package improves its accuracy and coverage. Diphthongization, for example, is sensitive to phonotactics. A word such as CV.ˈV.CV will be narrowly transcribed as ˈCGV.CV (except when the initial consonant is an affricate (allophonic), which seems to lower the probability of diphthongization based on my judgement). Diphthongization is not applied if the onset is complex. Needless to say, these assumptions are based on a particular dialect of Brazilian Portuguese, and I do not expect all of them to seamlessly apply to other dialects (although some assumptions are more easily generalizable than others).

Narrow transcription also includes (final) vowel reduction, voicing assimilation, l-vocalization, vowel devoicing, palatalization, and epenthesis in sC clusters and other consonant sequences that are expected to be repaired on surface forms (e.g., kt, gn). Examples can be generated with the function ipa_pt_test().

If you plan to tokenize texts and create a table with individual columns for stress and syllables, you can use some simple additional helper functions. For example, getWeight() will take a syllabified word and return its weight profile (e.g., getWeight("kon.to") will return HL). The function getStress()¹ will return the stress position of a given word (up to preantepenultimate stress)---the word must already be stressed, but the symbol used can be specified in the function (argument stress). Finally, countSyl() will return the number of syllables in a given string, and getSyl() will extract a particular syllable from a string. For example, getSyl(word = "kom-pu-ta-doɾ", pos = 3, syl = "-") will take the antepenultimate syllable of the string in question. The default symbol for syllabification is the period.

There are three functions in the package to analyze sonority. First, demi(word = ..., d = ...) extracts either the first (d = 1, the default) or second (d = 2) demisyllables of a given (syllabified) word (or vector of words. Second, sonDisp(demi = ...) calculates the sonority dispersion score of a given demisyllable, based on Clements (1990)—see also Parker (2011). Note that this metric does not differentiate sequences that respect the sonority sequencing principle (SSP) from those that don’t, i.e., pla and lpa will have the same score. For that reason, a third function exists, ssp(demi = ..., d = ...), which evaluates whether a given demisyllable respects (1) or doesn’t repect (0) the SSP. In the example below, the dispersion score of the first demisyllable in the penult syllable is calculated—ssp() isn’t relevant here, since all words in Portuguese respect the SSP.

example = tibble(word = c("partolo", "metrilpo", "vanplidos"))

example = example |> 
  rowwise() |> 
  mutate(ipa = ipa(word),
         syl2 = getSyl(word = ipa, pos = 2),
         demi1 = demi(word = syl2, d = 1),
         disp = sonDisp(demi = demi1),
         SSP = ssp(demi = demi1, d = 1))

example
#> # A tibble: 3 × 6
#> # Rowwise: 
#>   word      ipa          syl2  demi1  disp   SSP
#>   <chr>     <chr>        <chr> <chr> <dbl> <dbl>
#> 1 partolo   paɾ.ˈto.lo   to    to     0.06     1
#> 2 metrilpo  me.ˈtɾil.po  tɾil  tɾi    0.56     1
#> 3 vanplidos vam.ˈpli.dos pli   pli    0.56     1

You may also want to calculate the average sonority dispersion for whole words with the function meanSonDisp(). If your words of interest are possible or real Portuguese words, they can be entered in their ortographic form. Otherwise, they need to be phonemically transcribed and syllabified. In this scenario, use phonemic = T.

meanSonDisp(word = c("partolo", "metrilpo", "vanplidos"))
#> [1] 1.53

The function plotVowels() creates a vowel trapezoid using ggplot2 and also returns the LaTeX code to create the same trapezoid using the vowel package. Available languages: Arabic, French, English, Dutch, German, Hindi, Italian, Japanese, Korean, Mandarin, Portuguese, Spanish, Swahili, Russian, Talian, Thai, and Vietnamese

The function biGram_pt() returns the log bigram probability for a possible word in Portuguese (word must be broadly transcribed). The string must use broad phonemic transcription, but no syllabification or stress. The reference used calculate probabilities is the Portuguese Stress Lexicon.

Two additional functions can be used to explore bigrams: nGramTbl() generates a tibble with phonotactic bigrams from a given text, and plotnGrams() creates a plot for inputs generated with nGramTbl().

The function wug_pt() generates a hypothetical word in Portuguese. Note that this function is meant to be used to get you started with nonce words. You will most likely want to make adjustments based on phonotactic preferences. The function already takes care of some OCP effects and it also prohibits more than one onset cluster per word, since that’s relatively rare in Portuguese. Still, there will certainly be other sequences that sound less natural. The function is not too strict because you may have a wide range of variables in mind as you create novel words. Finally, if you wish to include palatalization, set palatalization = T—if you do that, bear in mind that biGram_pt() won’t work as it requires phonemic transcription without syllabification or stress.

set.seed(1)
wug_pt(profile = "LHL")
#> [1] "dɾa.ˈbuɾ.me"

# Let's create a table with 5 nonce words and their bigram probability
set.seed(1)
tibble(word = character(5)) |>
  mutate(word = wug_pt("LHL", n = 5),
         bigram = word |> biGram_pt())
#> # A tibble: 5 × 2
#>   word        bigram
#>   <chr>        <dbl>
#> 1 dɾa.ˈbuɾ.me  -49.2
#> 2 ze.ˈfɾan.ka  -50.7
#> 3 be.ˈʒan.tɾe  -49.2
#> 4 ʒa.ˈgɾan.fe  -51.9
#> 5 me.ˈxes.vɾo  -68.8

Additional functions include monthsAge() and meanAge(), both of which can be used to convert and analyze ages following the format yy;mm, commonly used in first language acquisition studies. It’s a good idea to check out the index of functions (?Fonology) to take a look at the complete list of functions available.

• Clements, G. N. 1990. The role of the sonority cycle in core syllabification. In John Kingston & Mary E. Beckman (eds.) Papers in laboratory phonology I: Between the grammar and physics of speech, 283–333. Cambridge: Cambridge University Press.

• Garcia, G. D. (2014). Portuguese Stress Lexicon. Available at gdgarcia.ca/psl.html.

• Garcia, G. D. (2017). Weight effects on stress: Lexicon and grammar [PhD thesis, McGill University]. https://doi.org/10.31219/osf.io/bt8hk

• Garcia, G. D. (2017). Weight gradience and stress in Portuguese. Phonology, 34(1), 41–79. https://doi.org/10.1017/S0952675717000033

• Garcia, G. D. (2019). When lexical statistics and the grammar conflict: Learning and repairing weight effects on stress. Language, 95(4), 612–641. https://doi.org/10.1353/lan.2019.0068

• Parker, S. (2011). Sonority. In M. van Oostendorp, C. J. Ewen, E. Hume, & K. Rice (Eds.), The Blackwell companion to phonology (pp. 1160–1184). Wiley Online Library. https://doi.org/10.1002/9781444335262.wbctp0049

• Wetzels, L., (2007) “Primary Word Stress in Brazilian Portuguese and the Weight Parameter”, Journal of Portuguese Linguistics 6(1), 9-58. doi: https://doi.org/10.5334/jpl.144