In congzhang365/rhythm.metrics: Analyse and Visualise Rhythm Metrics

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

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List of Functions

Calculations

• delta_cv
• varco_cv
• percentage_v
• rpvi_c
• npvi_v

Plotting

• plot_delta_cv
• plot_varco_cv
• plot_percentage_v
• plot_rpvi
• plot_npvi

Installation

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

Import packages

library(rhythm.metrics)
library(dplyr)

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Examples

Create dataframe

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(0.1, 0.8, 0.2, 0.5, 
                                  0.3, 0.3, 0.4, 0.7,
                                  0.3, 0.88, 0.5, 0.9, 
                                  0.3, 0.57, 0.4, 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))
df

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delta_cv

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: SD of total C duration
Delta V: SD of total V duration

delta_cv(df, df$cv_label, df$utterance_id, df$cv_duration)

plot_delta_cv(df, cv_label, utterance_id, cv_duration)

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varco_cv

Varco C and Varco V are rhythm metrics based on Dellwo, Volker (2006). Rhythm and Speech Rate: A Variation Coefficient for deltaC. In: Karnowski, P; Szigeti, I. Language and language-processing. Frankfurt/Main: Peter Lang, 231-241.

Varco C: Delta C / mean(C duration) * 100
Varco V: Delta V / mean(V duration) * 100

varco_cv(df)

plot_varco_cv(df, cv_label, utterance_id, cv_duration)

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percentage_v

%V is a rhythm metrics based on Ramus, F., Nespor, M., & Mehler, J. (1999). Correlates of linguistic rhythm in the speech signal. Cognition, 73(3), 265-292. It calculates the ratio of vocalic material to the total duration of an utterance.

% V: total V duration / total utterance duration

percentage_v(df, v_label="vowel")

plot_percentage_v(df, v_label="vowel", utterance_id, cv_duration)

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rpvi_c

rPVI C is a rhythm metrics 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 sum of the absolute differences between pairs of consecutive consonantal intervals divided by the number of pairs in the speech sample.

rpvi_c(df, c_label="consonant")

plot_rpvi(df, c_label="consonant", utterance_id, cv_duration)

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npvi_v

nPVI V is a rhythm metrics 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 sum of the absolute differences between pairs of consecutive vocalic intervals divided by the number of pairs in the speech sample.

npvi_v(df, v_label="vowel")

plot_npvi(df, v_label="vowel", utterance_id, cv_duration)

congzhang365/rhythm.metrics documentation built on March 31, 2024, 5:57 p.m.