phontrast

phontrast is an R package for measuring the separation and overlap of phonological categories in acoustic space, using multiple complementary contrast metrics through one interface. It is designed for researchers in sociophonetics, laboratory phonology, bilingualism, and speech perception who need principled, distributional measures of category contrast.

The package’s entry point, phontrast(), computes and compares a family of metrics for a two-category contrast — Jensen–Shannon divergence and distance, the Pillai–Bartlett trace, Bhattacharyya distance and affinity, Mahalanobis distance, and proportional overlap — globally or by group, with optional bootstrap confidence intervals. Because the metrics differ in what they capture (distribution shape vs. mean separation vs. overlap), reporting several together gives a fuller picture of a contrast than any one alone.

Formerly phonJSD. phontrast is the continuation of the phonJSD package (through v1.2.0), broadened from a Jensen–Shannon-divergence focus to a general multi-metric contrast toolkit. The estimators are unchanged; see NEWS.md for migration notes, and ROADMAP.md in the repository for what’s planned next.

Motivation

Traditional measures of phonological contrast (e.g., Euclidean distance between means, Pillai scores) each capture only limited aspects of category structure. phontrast treats phonological categories as probability distributions in acoustic space and brings several contrast measures — information-theoretic, parametric, and mean-based — under one roof, so you can compare them on a common footing instead of committing to a single metric up front.

This approach is especially useful when: - Categories differ in shape, variance, or multimodality - Token counts are unbalanced - Category overlap is gradient rather than categorical - You want measures that generalize naturally to multidimensional features (e.g., MFCCs)

Core Features

One call, many metrics: phontrast() computes Jensen–Shannon divergence and distance, Pillai–Bartlett trace, Bhattacharyya distance/affinity, Mahalanobis distance, and percent overlap side by side
Pick any subset with the metrics argument
Kernel density–based estimation of acoustic distributions for the distributional metrics, with an optional multivariate-normal backend (density = "mvnorm")
Support for 1D and n-dimensional acoustic features
Optional high-dimensional KDE speed controls, including diagonal Scott bandwidths, sampled evaluation points, and a fast diagonal-Gaussian engine
Global and group-level bootstrap summaries
A common orientation / separation_value scale so overlap and separation metrics can be read together
Distribution-aware, accountable visualization: plot_contrast() draws the same density model the metrics use (KDE highest-density regions or fitted-Gaussian ellipses), shades the overlap itself, and annotates panels with the metric values; plus metric-table, category-space, and PCA plots, all on a colorblind-safe Okabe–Ito palette (theme_phontrast())
Reproducible pipelines compatible with tidyverse workflows

Quick Start

Most users should start with phontrast(). It takes a token-level vowel table, compares two category labels in a numeric acoustic space, and returns the contrast metrics side by side.

library(phontrast)

set.seed(2026)
vowels <- data.frame(
  speaker = rep(c("s01", "s02"), each = 80),
  vowel = rep(rep(c("ih", "eh"), each = 40), 2),
  f1 = c(
    rnorm(40, 500, 55), rnorm(40, 560, 60),
    rnorm(40, 510, 60), rnorm(40, 575, 65)
  ),
  f2 = c(
    rnorm(40, 1980, 150), rnorm(40, 1880, 155),
    rnorm(40, 1960, 160), rnorm(40, 1840, 165)
  )
)

phontrast(
  data = vowels,
  features = c("f1", "f2"),
  category_col = "vowel",
  group_col = "speaker"
)

Select specific metrics, or switch to tidy long output for plotting and ranking:

metrics_long <- phontrast(
  data = vowels,
  features = c("f1", "f2"),
  category_col = "vowel",
  group_col = "speaker",
  metrics = c("jsd", "pillai", "overlap"),
  output = "long"
)

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

If ggplot2 is installed, the same workflow can be visualized directly. The flagship visualization is plot_contrast(), a distribution-aware, accountable view of one contrast: it draws the same density model the distributional metrics are computed from (KDE highest-density regions by default, coverage ellipses of the fitted Gaussians under density = "mvnorm"), shades the density overlap itself, and labels each panel with the Jensen–Shannon divergence and proportional overlap computed under the plotted model. The caption records the estimator settings, and the full metric table is attached as attr(p, "contrast_metrics").

plot_contrast(
  data = vowels,
  features = c("f2", "f1"),
  category_col = "vowel",
  group_col = "speaker",
  reverse_x = TRUE,
  reverse_y = TRUE
)

phontrast() results plot themselves — plot(metrics_long) (or ggplot2::autoplot()) draws the metric comparison via plot_overlap_metrics():

plot(metrics_long)

All plots share a colorblind-safe Okabe–Ito palette and a publication theme; theme_phontrast(), scale_color_phontrast(), scale_fill_phontrast(), and phontrast_palette() are exported so your own figures can match. Simple exploratory views remain available via plot_category_space().

For multidimensional features such as MFCCs or acoustic embeddings, compute metrics on the full feature set and use PCA plots as a diagnostic projection:

features <- paste0("mfcc", 1:13)

metrics_13d <- phontrast(
  data = vowel_tokens,
  features = features,
  category_col = "vowel",
  output = "long"
)

plot_category_pca(
  data = vowel_tokens,
  features = features,
  category_col = "vowel"
)

plot_category_pca() helps visualize high-dimensional structure, but it does not change the estimand: the metric table above is still estimated in all 13 dimensions.

For larger high-dimensional datasets, the default KDE path is conservative: it uses ks::Hpi() and evaluates on all pooled observations. You can opt into a faster nonparametric KDE path by using a diagonal Scott bandwidth, a sampled set of evaluation points, and the chunked diagonal evaluator:

metrics_13d_fast <- phontrast(
  data = vowel_tokens,
  features = paste0("mfcc", 1:13),
  category_col = "vowel",
  bw = "scott.diag",
  eval_on = "pooled_sample",
  eval_n = 200,
  eval_seed = 2026,
  engine = "fast_diag",
  output = "long"
)

These KDE controls affect only the distributional metrics (JSD and percent overlap): one kernel is still centered on each observed token, and the speedup comes from a cheaper diagonal bandwidth rule, evaluating densities on a bounded sampled support, and a vectorized diagonal-Gaussian evaluator. The package default is unchanged for backward compatibility.

Choosing the density backend

The distributional metrics (Jensen–Shannon divergence and percent overlap) are computed from a density estimate for each category. By default that estimate is a kernel density (density = "kde"). You can instead fit one multivariate normal per category with density = "mvnorm":

phontrast(
  data = vowels,
  features = c("f1", "f2"),
  category_col = "vowel",
  density = "mvnorm"
)

Under density = "mvnorm" the two categories are modelled as Gaussians and the JSD and overlap between them are estimated by Monte-Carlo (Jensen–Shannon divergence between two Gaussians has no closed form); mc_n sets the number of Monte-Carlo samples and eval_seed makes the estimate reproducible. This decouples the density estimator from the metric, so the estimator behind JSD and overlap can be matched to the same multivariate-normal assumptions that the Pillai, Bhattacharyya, and Mahalanobis columns already make — useful when comparing metrics on a common footing, or for higher-dimensional feature spaces where multivariate KDE is impractical. The Pillai, Bhattacharyya, and Mahalanobis columns are parametric by construction and are unaffected by this argument. density is available on phontrast(), estimate_jsd(), estimate_overlap(), jsd_summary(), global_boot_jsd(), jsd_kde_nd(), and percent_overlap_kde().

If you only need the package’s information-theoretic estimate on its own, use estimate_jsd():

estimate_jsd(
  data = vowels,
  features = c("f1", "f2"),
  category_col = "vowel",
  group_col = "speaker",
  do_boot = TRUE,
  n_boot = 100
)

Use lower-level helpers such as jsd_kde_nd(), percent_overlap_kde(), pillai_overlap(), and bhattacharyya_mvnorm() when you are validating a method, debugging one contrast, or need direct control over one metric.

compare_overlap_metrics() from phonJSD still works but is deprecated: it now calls phontrast() with output = "wide". Switch calls to phontrast().

Conceptual Overview

Several of phontrast’s metrics are distributional. For the Jensen–Shannon family, given two phonological categories (e.g., vowels /ɪ/ and /ɛ/), phontrast:

Represents each category as a probability distribution over acoustic space
Estimates densities using kernel density estimation (KDE)
Computes Jensen–Shannon Divergence between the distributions

KDE is the package’s default density-estimation strategy for continuous acoustic spaces. JSD itself, however, operates on probability distributions and is not intrinsically tied to KDE. Manuscript sensitivity analyses show that the same substantive contrast pattern is recovered when JSD is computed from KDE, histogram-based, empirical-binned, and Gaussian-mixture distribution estimates.

By default (method = "mc") the divergence is estimated with a Monte-Carlo plug-in: each category’s KDE is evaluated at that category’s own tokens and the log density ratio against the mixture is averaged, giving a consistent estimate of the continuous JSD in any number of dimensions. A sample-size-scaled partial leave-one-out correction reduces resubstitution bias while keeping small real divergences as small positive values rather than flooring them to exactly 0. The pre-1.2.0 self-normalized sample-point estimate — a bounded relative separation index rather than the JSD integral — remains available as method = "legacy" for reproducing 1.0.0 results.

JSD values: - 0 → complete overlap (no separation) - Higher values → greater distributional separation - Bounded and symmetric, making cross-study comparisons more interpretable

Choosing and Interpreting Metrics

The metrics are not all oriented in the same direction:

Metric	Direction	Scale	Best used when	Main caveat
JSD	Higher = more separation	0-1 bits	You want a bounded distributional separation metric	KDE estimates need adequate sample size per category
Jensen-Shannon distance	Higher = more separation	0-1	You want a distance transform of JSD	Same estimator caveats as JSD
Pillai trace	Higher = more separation	0-1	You want a classical MANOVA-style comparison	Primarily mean-based; less sensitive to distribution shape
Bhattacharyya distance	Higher = more separation	0 to infinity	You want a parametric distribution-distance comparison	Assumes approximately multivariate normal categories
Mahalanobis distance	Higher = more separation	0 to infinity	You want mean separation scaled by covariance	Sensitive to covariance estimation and small samples
Percent overlap	Higher = more overlap	0-1 proportion	You want a directly interpretable shared-density estimate	Despite the name, output is a proportion, not 0-100
Bhattacharyya affinity	Higher = more overlap	0-1	You want a parametric overlap analogue	Assumes approximately multivariate normal categories

For side-by-side comparison, phontrast(output = "long") adds orientation, separation_value, and separation_rank columns so overlap metrics can be read on the same separation-oriented scale as distance metrics. Confidence interval columns are named ci_lower and ci_upper; legacy JSD-specific aliases jsd_low and jsd_high are retained for compatibility.

PB52 Example Note

The Peterson and Barney 1952 data in phonTools::pb52 are useful for global vowel contrasts. After filtering the factor-valued vowel column, phontrast counts observed levels only, so manual droplevels() is not required.

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"
)

Do not use per-speaker F1/F2 I/i overlap metrics in PB52 without a coarser grouping or different design: each speaker has only two repetitions per vowel, which is too few for KDE-based two-dimensional overlap estimates.

Installation

This package is not yet on CRAN. Install the latest tagged release with:

# install.packages("remotes")
remotes::install_github("berrygrant/phontrast@v2.3.1")

For the current development version, use:

remotes::install_github("berrygrant/phontrast")

AI Use Disclosure

OpenAI ChatGPT/Codex and Anthropic Claude Code assisted with code review, documentation drafting, test generation, and implementation support during development of this package. All package design decisions, analyses, validation, and release decisions were reviewed and directed by the maintainer.