Multidimensional workflows
In phontrast: Contrast and Separation Metrics for Phonological Categories

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

phontrast can estimate category separation in feature spaces with more than two dimensions, such as MFCC vectors, formants plus duration, or other acoustic embeddings. The key distinction is that metrics and plots answer different questions:

phontrast() estimates metrics in the full feature space you pass to features.
plot_category_space() shows one or two selected dimensions.
plot_category_pca() shows a two-dimensional PCA projection for diagnosing a multidimensional space.

library(phontrast)

set.seed(2026)
features <- paste0("feature_", 1:6)
tokens <- data.frame(
  speaker = rep(c("s01", "s02"), each = 80),
  category = rep(rep(c("A", "B"), each = 40), 2),
  matrix(rnorm(160 * length(features)), ncol = length(features))
)
names(tokens)[-(1:2)] <- features
tokens[tokens$category == "B", features[1:3]] <-
  tokens[tokens$category == "B", features[1:3]] + 0.65

Compute metrics in all dimensions.

metrics <- phontrast(
  data = tokens,
  features = features,
  category_col = "category",
  group_col = "speaker",
  output = "long"
)

metrics[, c("group", "metric", "estimate", "orientation", "separation_value")]

Visualize a projection without changing the metric estimand.

plot_category_pca(
  data = tokens,
  features = features,
  category_col = "category",
  group_col = "speaker"
)

For a specific pair of interpretable dimensions, use plot_category_space().

plot_category_space(
  data = tokens,
  features = c("feature_1", "feature_2"),
  category_col = "category",
  group_col = "speaker"
)

PCA plots are useful for sanity checks and presentations, but they can hide separation that lives outside the first two principal components. Use them as a visual diagnostic; report metric estimates from the intended full feature set.

PB52 note

The Peterson and Barney 1952 data in phonTools::pb52 are useful for global vowel contrasts. For example, a global I/i comparison in F1/F2 has many tokens. Per-speaker I/i F1/F2 comparisons are not estimable with KDE-based metrics because each speaker has only two repetitions per vowel.

data(pb52, package = "phonTools")
pb_i <- subset(pb52, as.character(vowel) %in% c("I", "i"))

phontrast(
  data = pb_i,
  features = c("f1", "f2"),
  category_col = "vowel"
)