Tools for Tidy Vowel Normalization

An implementation of tidy speaker vowel normalization. This includes generic functions for defining new normalization methods for points, formant tracks, and Discrete Cosine Transform coefficients, as well as convenience functions implementing established normalization methods. References for the implemented methods are: Johnson, Keith (2020) Lobanov, Boris (1971) Nearey, Terrance M. (1978) < https://sites.ualberta.ca/~tnearey/Nearey1978_compressed.pdf> Syrdal, Ann K., and Gopal, H. S. (1986) Watt, Dominic, and Fabricius, Anne (2002) < https://www.latl.leeds.ac.uk/article/evaluation-of-a-technique-for-improving-the-mapping-of-multiple-speakers-vowel-spaces-in-the-f1-f2-plane/>.

tidynorm

The goal of {tidynorm} is to provide convenient and tidy functions to normalize vowel formant data.

Installation

You can install tidynorm like so

install.packages("tidynorm")

You can install the development version of tidynorm like so:

## if you need to install `remotes`
# install.packages("remotes")
remotes::install_github("jofrhwld/tidynorm")

Example

Vowel formant frequencies are heavily influenced by vocal tract length differences between speakers. Equivalent vowels between speakers can have dramatically different frequency locations.

library(tidynorm)
library(ggplot2)

options(
  ggplot2.discrete.colour = c(
    lapply(
      1:6,
      \(x) c(
        "#4477AA", "#EE6677", "#228833",
        "#CCBB44", "#66CCEE", "#AA3377"
      )[1:x]
    )
  ),
  ggplot2.discrete.fill = c(
    lapply(
      1:6,
      \(x) c(
        "#4477AA", "#EE6677", "#228833",
        "#CCBB44", "#66CCEE", "#AA3377"
      )[1:x]
    )
  )
)

theme_set(
  theme_minimal(
    base_size = 16
  )
)

ggplot(
  speaker_data,
  aes(
    F2, F1,
    color = speaker
  )
) +
  ggdensity::stat_hdr(
    probs = c(0.95, 0.8, 0.5),
    alpha = 1,
    fill = NA,
    linewidth = 1
  ) +
  scale_x_reverse() +
  scale_y_reverse() +
  coord_fixed() +
  labs(
    title = "unnormalized"
  )

The goal of {tidynorm} is to provide tidyverse-friendly and familiar functions that will allow you to quickly normalize vowel formant data. There are a number of built in functions based on conventional normalization methods.

speaker_data |>
  norm_nearey(
    F1:F3,
    .by = speaker,
    .names = "{.formant}_nearey"
  ) ->
speaker_normalized

#> Normalization info
#> • normalized with `tidynorm::norm_nearey()`
#> • normalized `F1`, `F2`, and `F3`
#> • normalized values in `F1_nearey`, `F2_nearey`, and `F3_nearey`
#> • grouped by `speaker`
#> • within formant: FALSE
#> • (.formant - mean(.formant, na.rm = T))/(1)

speaker_normalized |>
  ggplot(
    aes(
      F2_nearey, F1_nearey,
      color = speaker
    )
  ) +
  ggdensity::stat_hdr(
    probs = c(0.95, 0.8, 0.5),
    alpha = 1,
    fill = NA,
    linewidth = 1
  ) +
  scale_x_reverse() +
  scale_y_reverse() +
  coord_fixed() +
  labs(
    title = "Nearey Normalized"
  )

There is also a tidynorm::norm_generic() function to allow you to define your own bespoke normalization methods. For example, a “robust Nearey” normalization method using the median, instead of the mean, could be done like so.

speaker_rnearey <- speaker_data |>
  norm_generic(
    F1:F3,
    .by = speaker,
    .by_formant = FALSE,
    .pre_trans = log,
    .L = median(.formant, na.rm = T),
    .names = "{.formant}_rnearey"
  )

#> Normalization info
#> • normalized with `tidynorm::norm_generic()`
#> • normalized `F1`, `F2`, and `F3`
#> • normalized values in `F1_rnearey`, `F2_rnearey`, and `F3_rnearey`
#> • grouped by `speaker`
#> • within formant: FALSE
#> • (.formant - median(.formant, na.rm = T))/(1)

speaker_rnearey |>
  ggplot(
    aes(
      F2_rnearey, F1_rnearey,
      color = speaker
    )
  ) +
  ggdensity::stat_hdr(
    probs = c(0.95, 0.8, 0.5),
    alpha = 1,
    fill = NA,
    linewidth = 1
  ) +
  scale_x_reverse() +
  scale_y_reverse() +
  coord_fixed() +
  labs(
    title = "Robust Nearey Normalized"
  )