Skip to contents

Delta F Track Normalization

Source: R/dct_norm.R
norm_track_deltaF.Rd

Delta F Track Normalization

Usage

norm_track_deltaF(
  .data,
  ...,
  .token_id_col,
  .by = NULL,
  .time_col = NULL,
  .order = 5,
  .return_dct = FALSE,
  .drop_orig = FALSE,
  .names = "{.formant}_df",
  .silent = FALSE
)

Arguments

.data

A data frame containing vowel formant data

...

<tidy-select> One or more unquoted expressions separated by commas. These should target the vowel formant data columns.

.token_id_col

<data-masking> A column that identifies token ids.

.by

<tidy-select> A selection of columns to group by. Typically a column of speaker IDs.

.time_col

<data-masking> A time column. (optional)

.order

The number of DCT parameters to use.

.return_dct

Whether or not the normalized DCT coefficients themselves should be returned.

.drop_orig

Should the originally targeted columns be dropped.

.names

A glue::glue() expression for naming the normalized data columns. The "{.formant}" portion corresponds to the name of the original formant columns.

.silent

Whether or not the informational message should be printed.

Value

A data frame of Delta F normalized formant tracks.

Details

$$ \hat{F}_{ij} = \frac{F_{ij}}{S} $$ $$ S = \frac{1}{MN}\sum_{i=1}^M\sum_{j=1}^N \frac{F_{ij}}{i-0.5} $$

Where

  • \(\hat{F}\) is the normalized formant

  • \(i\) is the formant number

  • \(j\) is the token number

References

Johnson, K. (2020). The ΔF method of vocal tract length normalization for vowels. Laboratory Phonology: Journal of the Association for Laboratory Phonology, 11(1), Article 1. https://doi.org/10.5334/labphon.196

Examples

library(tidynorm)
library(dplyr)
ggplot2_inst <- require(ggplot2)

track_subset <- speaker_tracks |>
  filter(
    .by = c(speaker, id),
    if_all(
      F1:F3,
      .fns =\(x) mean(is.finite(x)) > 0.9
    ),
    row_number() %% 2 == 1
  )

track_norm <- track_subset |>
  norm_track_deltaF(
    F1:F3,
    .by = speaker,
    .token_id_col = id,
    .time_col = t,
    .drop_orig = TRUE
  )

if(ggplot2_inst){
  track_norm |>
    ggplot(
      aes(F2_df, F1_df, color = speaker)
    )+
    stat_density_2d(bins = 4)+
    scale_x_reverse()+
    scale_y_reverse()+
    scale_color_brewer(palette = "Dark2")+
    coord_fixed()
}



# returning the DCT coefficients
track_norm_dct <- track_subset |>
  norm_track_deltaF(
    F1:F3,
    .by = speaker,
    .token_id_col = id,
    .time_col = t,
    .drop_orig = TRUE,
    .return_dct = TRUE,
    .names = "{.formant}_df"
  )

track_norm_means <- track_norm_dct |>
  summarise(
    .by = c(speaker, vowel, .param),
    across(
      ends_with("_df"),
      mean
    )
  ) |>
  reframe_with_idct(
    ends_with("_df"),
    .by = speaker,
    .token_id_col = vowel,
    .param_col = .param
  )


if(ggplot2_inst){
  track_norm_means|>
    ggplot(
      aes(F2_df, F1_df, color = speaker)
    )+
    geom_path(
      aes(
        group = interaction(speaker, vowel)
      )
    )+
    scale_x_reverse()+
    scale_y_reverse()+
    scale_color_brewer(palette = "Dark2")+
    coord_fixed()
}