Skip to contents

rhythm.metrics

rhythm.metrics logo

CRAN_Status_Badge License: GPL v3

rhythm.metrics: Analyse and Visualise Speech Rhythm and Timing Metrics

This package calculates and visualises speech rhythm and timing metrics from consonantal and vocalic interval durations. It provides a standardised workflow to compute common metrics including Delta C, Delta V, Varco C, Varco V, %V, rPVI-C, and nPVI-V, as well as plotting functions for exploring and presenting results.

Note: rhythm.metrics is under slow but active development, and additional metrics may be added in future releases.

Features

  • Calculate common rhythm metrics from interval-duration data
  • Summarise rhythm metrics across utterances
  • Visualise rhythm metrics with ready-to-use plotting functions
  • Work directly with simple data frames

Installation

You can install the released version of rhythm.metrics from CRAN with:

install.packages("rhythm.metrics")

Alternatively, you can install the development version from GitHub:

install.packages("remotes")
remotes::install_github("congzhang365/rhythm.metrics")

Load the Package

Input Data Format

The package expects a data frame containing interval labels, utterance identifiers, and interval durations.

A typical input data frame looks like this:

df <- data.frame(
  cv_label = c(
    "consonant", "vowel", "consonant", "vowel",
    "consonant", "vowel", "consonant", "vowel",
    "consonant", "vowel", "consonant", "vowel",
    "consonant", "vowel", "consonant", "vowel"
  ),
  utterance_id = c(
    "utt_1", "utt_1", "utt_1", "utt_1",
    "utt_2", "utt_2", "utt_2", "utt_2",
    "utt_3", "utt_3", "utt_3", "utt_3",
    "utt_4", "utt_4", "utt_4", "utt_4"
  ),
  cv_duration = c(
    0.10, 0.80, 0.20, 0.50,
    0.30, 0.30, 0.40, 0.70,
    0.30, 0.88, 0.50, 0.90,
    0.30, 0.57, 0.40, 0.97
  ),
  utterance_duration = c(
    2.4, 2.4, 2.4, 2.4,
    2.7, 2.7, 2.7, 2.7,
    3.4, 3.4, 3.4, 3.4,
    1.8, 1.8, 1.8, 1.8
  )
)

Available Functions

Category Function Description
Calculation delta_cv() Calculate Delta C and Delta V
Calculation varco_cv() Calculate Varco C and Varco V
Calculation percentage_v() Calculate percentage of vocalic intervals (%V)
Calculation rpvi_c() Calculate raw Pairwise Variability Index for consonants
Calculation npvi_v() Calculate normalised Pairwise Variability Index for vowels
Plotting plot_delta_cv() Plot Delta C and Delta V
Plotting plot_varco_cv() Plot Varco C and Varco V
Plotting plot_percentage_v() Plot %V
Plotting plot_rpvi() Plot rPVI-C
Plotting plot_npvi() Plot nPVI-V

Quick Start

Delta C and Delta V

Delta C and Delta V are rhythm metrics based on:

Ramus, F., Nespor, M., & Mehler, J. (1999). Correlates of linguistic rhythm in the speech signal. Cognition, 73(3), 265-292.

  • Delta C: standard deviation of consonantal interval durations
  • Delta V: standard deviation of vocalic interval durations
delta_cv(df, cv_label, utterance_id, cv_duration)
plot_delta_cv(df, cv_label, utterance_id, cv_duration)

Varco C and Varco V

Varco C and Varco V are based on:

Dellwo, V. (2006). Rhythm and Speech Rate: A Variation Coefficient for deltaC. In P. Karnowski & I. Szigeti (Eds.), Language and language-processing (pp. 231-241). Peter Lang.

  • Varco C: Delta C / mean consonant duration * 100
  • Varco V: Delta V / mean vowel duration * 100
varco_cv(df, cv_label, utterance_id, cv_duration)
plot_varco_cv(df, cv_label, utterance_id, cv_duration)

Percentage of Vocalic Intervals (%V)

%V is based on:

Ramus, F., Nespor, M., & Mehler, J. (1999). Correlates of linguistic rhythm in the speech signal. Cognition, 73(3), 265-292.

It measures the percentage of total utterance duration occupied by vocalic material.

percentage_v(df, v_label = "vowel", utterance_id, cv_duration, utterance_duration)
plot_percentage_v(df, cv_label, label_name = "vowel", 
                  utterance_id, cv_duration, utterance_duration)

rPVI-C

rPVI-C is based on:

Grabe, E., & Low, E. L. (2002). Durational variability in speech and the rhythm class hypothesis. In Laboratory Phonology 7 (pp. 515-546). De Gruyter Mouton.

It calculates the average absolute difference between consecutive consonantal intervals.

rpvi_c(df, cv_label, label_name = "consonant", utterance_id, cv_duration)
plot_rpvi(df, cv_label, label_name = "consonant", utterance_id, cv_duration)

nPVI-V

nPVI-V is based on:

Grabe, E., & Low, E. L. (2002). Durational variability in speech and the rhythm class hypothesis. In Laboratory Phonology 7 (pp. 515-546). De Gruyter Mouton.

It calculates the normalised average absolute difference between consecutive vocalic intervals.

npvi_v(df, cv_label, label_name = "vowel", utterance_id, cv_duration)
plot_npvi(df, cv_label, label_name = "vowel", utterance_id, cv_duration)

Example Workflow

A simple workflow with the package might look like this:

library(rhythm.metrics)

df <- data.frame(
  cv_label = c(
    "consonant", "vowel", "consonant", "vowel",
    "consonant", "vowel", "consonant", "vowel",
    "consonant", "vowel", "consonant", "vowel",
    "consonant", "vowel", "consonant", "vowel"
  ),
  utterance_id = c(
    "utt_1", "utt_1", "utt_1", "utt_1",
    "utt_2", "utt_2", "utt_2", "utt_2",
    "utt_3", "utt_3", "utt_3", "utt_3",
    "utt_4", "utt_4", "utt_4", "utt_4"
  ),
  cv_duration = c(
    0.10, 0.80, 0.20, 0.50,
    0.30, 0.30, 0.40, 0.70,
    0.30, 0.88, 0.50, 0.90,
    0.30, 0.57, 0.40, 0.97
  ),
  utterance_duration = c(
    2.4, 2.4, 2.4, 2.4,
    2.7, 2.7, 2.7, 2.7,
    3.4, 3.4, 3.4, 3.4,
    1.8, 1.8, 1.8, 1.8
  )
)

# Analysis
delta_cv(df, cv_label, utterance_id, cv_duration)
varco_cv(df, cv_label, utterance_id, cv_duration)
percentage_v(df, v_label = "vowel", utterance_id, cv_duration, utterance_duration)
rpvi_c(df, cv_label, label_name = "consonant", utterance_id, cv_duration)
npvi_v(df, cv_label, label_name = "vowel", utterance_id, cv_duration)

# Visualisation
plot_delta_cv(df, cv_label, utterance_id, cv_duration)
plot_varco_cv(df, cv_label, utterance_id, cv_duration)
plot_percentage_v(df, cv_label, label_name = "vowel", 
                  utterance_id, cv_duration, utterance_duration)
plot_rpvi(df, cv_label, label_name = "consonant", utterance_id, cv_duration)
plot_npvi(df, cv_label, label_name = "vowel", utterance_id, cv_duration)

Documentation

For more detailed examples and usage notes, see the package vignette and function help pages after installation.

?delta_cv
?varco_cv
?percentage_v
?rpvi_c
?npvi_v

Citation

If you use rhythm.metrics in your research, please cite:

Zhang, C. (2022). A Guide for the R Package “rhythm_metrics”. OSF Preprints. https://doi.org/10.31219/osf.io/kfnzt

You can also obtain the package citation from R with:

citation("rhythm.metrics")

BibTeX for the 2022 guide:

@misc{zhang2022,
  title  = {A Guide for the R Package "rhythm_metrics"},
  author = {Zhang, Cong},
  year   = {2022},
  doi    = {10.31219/osf.io/kfnzt},
  url    = {https://doi.org/10.31219/osf.io/kfnzt}
}

Research Using rhythm.metrics

The following studies have used rhythm.metrics in their analysis:

  • Laméris, T. J., & Kubota, M. (2026). L1 phonetic reversal and L2 phonetic attrition in Japanese–English bilingual returnee children over the course of five years: an acoustic study. Second Language Research. https://doi.org/10.1177/02676583261461940
  • Sun, Y., & Zhang, C. (2022). Task effect on L2 rhythm production by Cantonese learners of Portuguese. DELTA: Documentação de Estudos em Lingüística Teórica e Aplicada, 38(3), 202258943. https://doi.org/10.1590/1678-460X202258943

If you have published work using rhythm.metrics and would like it listed here, please feel free to open an issue or pull request.

Contributing

Bug reports, feature requests, and suggestions are welcome. If you encounter an issue or would like to suggest an additional metric, please open an issue on GitHub or email me at

License

GPL-3