code_allophones: Code allophones

In JoeyStanley/joeyr: Functions for Vowel Data

Code allophones

Description

A function to classify vowel data into contextual allophones.

Usage

code_allophones(
  .df,
  .old_col,
  .new_cols = c("allophone", "allophone_environment"),
  .pre_seg,
  .fol_seg,
  .coronals = c("T", "D", "S", "Z", "SH", "ZH", "JH", "N"),
  .voiceless = c("P", "T", "K", "CH", "F", "TH", "S", "SH")
)

Arguments

.df	The dataset containing vowel data.
.old_col	The unquoted name of the column containing the vowel labels. Often called "vowel" or "phoneme" in many datasets. Note that the function assumes Wells lexical sets (FLEECE, TRAP, etc.) rather than ARPABET (IY, AE, etc.) or IPA (i, æ, etc.). If your vowels are not already coded using Wells' labels you can quickly do so with switch_transcriptions or one of the shortcuts like arpa_to_wells
.new_cols	A vector of two strings containing the names of the columns you would like to use. By default c("allophone", "allophone_environment"). The first name becomes the name of the column containing the new allophone labels. The second column becomes the name of the column describing those labels.
.pre_seg	The unquoted name of the column that contains the labels for the previous segement. In DARLA-generated spreadsheets, this is 'pre_seg' and in FastTrack-generated spreadsheets, it's 'previous_sound'. Assumes ARPABET labels.
.fol_seg	The unquoted name of the column that contains the labels for the following segement. In DARLA-generated spreadsheets, this is 'fol_seg' and in FastTrack-generated spreadsheets, it's 'next_sound'. Assumes ARPABET labels.
.coronals	A vector of strings containing ARPABET labels for coronal consonants. By default, c("T", "D", "S", "Z", "SH", "ZH", "JH", "N"). This is used to create the 'TOOT' allophone of 'GOOSE'.
.voiceless	A vector of strings containing ARPABET labels for voiceless consonants. By default, c("P", "T", "K", "CH", "F", "TH", "S", "SH"). This is used to create the 'PRICE' allophone of 'PRIZE'.

Value

A dataframe with two additional columns. One column contains labels for the allophones and the other contains category labels for those allophones' contexts. The second column can be useful for quickly excluding certain allophones like prelaterals or prerhotics or coloring families of allophones in visualizations (such as turning all prelateral allophones gray). These two new columns are positioned immediately after the original vowel column indicated in .old_col,

Note

Here are the list of the contextual allophones that are created. Note that I largely follow my own advice about what to call elsewhere allophones, what to call prelateral allophones, and other allophones. Obviously, this list is pretty subjective and largely based on what my own research has needed, so it may not work completely for you and your research. Please contact me at [e-mail] if you want to see an allophone get added or if you spot an error in the coding.

• FLEECE becomes

  • ZEAL before laterals

  • BEET elsewhere

• KIT becomes

  • GUILT before laterals

  • NEAR before rhotics

  • BIG before G

  • BIN before M and N

  • BING before NG

  • BIT elsewhere

• FACE becomes

  • FLAIL before laterals

  • VAGUE before G

  • BAIT elsewhere

• DRESS becomes

  • SHELF before laterals

  • SQUARE before rhotics

  • BEG before G

  • BEN before M and N

  • BENG before NG

  • BET elsewhere

• TRAP becomes

  • TALC before laterals

  • BAG before G

  • BAN before M and N

  • BANG before NG

  • BAT elsewhere

• LOT becomes

  • GOLF before laterals

  • START before rhotics

  • BOT elsewhere

• THOUGHT becomes

  • FAULT before laterals

  • FORCE befpre rhotics

  • BOUGHT elsewhere

• STRUT becomes

  • MULCH before laterals

  • BUT elsewhere

• GOAT becomes

  • JOLT before laterals

  • BOAT elsewhere

• FOOT becomes

  • WOLF before laterals

  • CURE before rhotics

  • PUT elsewhere

• GOOSE becomes

  • MULE before Y

  • TOOT before coronals

  • SPOOL before laterals

  • BOOT elsewhere

• PRICE becomes

  • PRICE before voiceless segments

  • PRIZE elsewhere

Unfortunately, it is not straightforward to customize this list but you can always copy the source code and modify the list yourself.

Alternatively, you can use forcats::fct_collapse() to collapse distinctions that you don't need. See example code below.

You can also of course create your own allophones if desired. Note that some allophones depend on other environmental information like syllable structure and morpheme/word boundaries, or they may be entirely lexical (FORCE vs. NORTH). They may be more complicated than what ARPABET can code for (MARY, MERRY, and MARRY) or just inconsistently coded. For the sake of simplicity, these allophones are not included in this function.

The environments therefore are the following

• "prelateral" includes ZEAL, GUILT, FLAIL, SHELF, TALC, GOLF, FAULT, MULCH, JOLT, WOLF, SPOOL

• "prerhotic" includes NEAR, SQUARE, START, FORCE, CURE

• "prevelar" includes BIG, VAGUE, BEG, BAG,

• "prenasal" includes BIN, BEN, BAN

• "prevelarnasal" includes BING, BENG, BANG

• "prevoiceless" includes PRICE

• "post-Y" includes MULE

• "postcoronal" includes TOOT

• "elsewhere" includes BEET, BIT, BAIT, BET, BAT, BOT, BOUGHT, BUT, BOAT, PUT, BOOT, PRIZE

Examples

suppressPackageStartupMessages(library(tidyverse))

# Get some sample DARLA data to play with
darla <- joeysvowels::darla %>%
  select(word, vowel, pre_seg, fol_seg) %>%
  mutate(phoneme = joeyr:::arpa_to_wells(vowel), .after = vowel)

# Basic usage
darla %>%
  code_allophones(.old_col = phoneme, .fol_seg = fol_seg, .pre_seg = pre_seg) %>%
  slice_sample(n = 20)

# Specify the names of the new columns with the `.new_cols` argument
darla %>%
  code_allophones(.old_col = phoneme,
                  .new_cols = c("allophone", "environment"),
                  .fol_seg = fol_seg,
                  .pre_seg = pre_seg) %>%
  slice_sample(n = 20)

# Filtering by environment is straightforward
darla %>%
  code_allophones(.old_col = phoneme,
                  .new_cols = c("allophone", "environment"),
                  .fol_seg = fol_seg,
                  .pre_seg = pre_seg) %>%
  filter(environment == "elsewhere") %>%
  slice_sample(n = 20)
darla %>%
  code_allophones(.old_col = phoneme,
                  .new_cols = c("allophone", "environment"),
                  .fol_seg = fol_seg,
                  .pre_seg = pre_seg) %>%
  filter(!environment %in% c("prerhotic", "prevelarnasal", "prevelar")) %>%
  slice_sample(n = 20)

# Some users may want to supply their own list of coronal consonants.
darla %>%
  code_allophones(.old_col = phoneme,
                  .new_cols = c("allophone", "environment"),
                  .fol_seg = fol_seg,
                  .pre_seg = pre_seg,
                  .coronals = c("T", "D", "S", "Z", "SH", "ZH", "JH", "N", "Y")) %>%
  filter(phoneme == "GOOSE") %>%
  slice_sample(n = 20)

# Other users may want to specify their own list of voiceless consonants.
darla %>%
  code_allophones(.old_col = phoneme,
                  .new_cols = c("allophone", "environment"),
                  .fol_seg = fol_seg,
                  .pre_seg = pre_seg,
                  .voiceless = c("P", "T", "K", "CH", "F", "TH", "S", "SH", "X")) %>%
  filter(phoneme == "PRICE") %>%
  slice_sample(n = 20)

# Collapsing distinctions can be done post hoc (though it may take extra work to get the environment column to match.)
darla %>%
  code_allophones(.old_col = phoneme,
                  .new_cols = c("allophone", "environment"),
                  .fol_seg = fol_seg,
                  .pre_seg = pre_seg) %>%
  # Get a subset for demonstration purposes
  filter(allophone %in% c("BIT", "BIG")) %>%
  group_by(allophone) %>%
  slice_sample(n = 5) %>%
  ungroup() %>%
  # Now collapse distinctions
  mutate(allophone = fct_collapse(allophone, "BIT" = c("BIT", "BIG")),
         environment = ifelse(allophone == "BIT", "elsewhere", allophone))

# Creating new allophones depends on the complexity of the allophone
darla %>%
  code_allophones(.old_col = phoneme,
                  .new_cols = c("allophone", "environment"),
                  .fol_seg = fol_seg,
                  .pre_seg = pre_seg) %>%
  # Create voice and voiceless distinctions for MOUTH
  mutate(allophone = case_when(phoneme == "MOUTH" & fol_seg %in% c("P", "T", "K", "CH", "F", "TH", "S", "SH") ~ "BOUT",
                               phoneme == "MOUTH" ~ "LOUD",
                               TRUE ~ allophone),
         environment = if_else(allophone == "BOUT",  "prevoiceless", environment)) %>%
  # Get a subset for demonstration purposes
  filter(phoneme == "MOUTH") %>%
  group_by(allophone) %>%
  slice_sample(n = 5) %>%
  ungroup()

JoeyStanley/joeyr documentation built on April 7, 2023, 8:37 p.m.