Normalizing Formant Tracks

    library(tidynorm)

    library(ggplot2)

Tidynorm normalizes vowel formant tracks by

1. Converting them to Discrete Cosine Transform coefficients.
2. Directly normalizing the DCT coefficients.
3. Applying the inverse DCT.

## Token IDs

In order to do this successfully, each vowel token needs to be uniquely identifiable with a token id, or a token id in combination with other columns. For example, in `tidynorm::speaker_tracks` the column `speaker` combined with the column `id` identifies each unique vowel formant track.

    speaker_tracks
    #> # A tibble: 19,500 × 9
    #>    speaker    id vowel plt_vclass word      t    F1    F2    F3
    #>    <chr>   <dbl> <chr> <chr>      <chr> <dbl> <dbl> <dbl> <dbl>
    #>  1 s01         0 EY    eyF        OKAY   32.4  754. 2145. 2913.
    #>  2 s01         0 EY    eyF        OKAY   32.4  719. 2155. 2913.
    #>  3 s01         0 EY    eyF        OKAY   32.4  752. 2115. 2914.
    #>  4 s01         0 EY    eyF        OKAY   32.4  762. 2087. 2931.
    #>  5 s01         0 EY    eyF        OKAY   32.4  738. 2088. 2933.
    #>  6 s01         0 EY    eyF        OKAY   32.4  697. 2123. 2936.
    #>  7 s01         0 EY    eyF        OKAY   32.4  640. 2143. 2928.
    #>  8 s01         0 EY    eyF        OKAY   32.5  540. 2153. 2936.
    #>  9 s01         0 EY    eyF        OKAY   32.5  441. 2164. 2936.
    #> 10 s01         0 EY    eyF        OKAY   32.5  497. 2201. 2945.
    #> # ℹ 19,490 more rows

For most normalization procedures, we’ll want to group the data by the speaker column anyway, and in those cases it’s sufficient to pass `id` to the `.token_id_col` argument of `norm_track_*` functions.

We can Lobanov normalize these speakers’ formant tracks with `norm_track_lobanov()`.

    normed_tracks <- speaker_tracks |>
      norm_track_lobanov(
        # identify the formant columns
        F1:F3,

        # provide speaker grouping
        .by = speaker,

        # provide token id
        .token_id_col = id,

        # provide an optional time column
        .time_col = t
      )
    #> Normalization info
    #> • normalized with `tidynorm::norm_track_lobanov()`
    #> • normalized `F1`, `F2`, and `F3`
    #> • normalized values in `F1_z`, `F2_z`, and `F3_z`
    #> • token id column: `id`
    #> • time column: `t`
    #> • grouped by `speaker`
    #> • within formant: TRUE
    #> • (.formant - mean(.formant, na.rm = T))/sd(.formant, na.rm = T)

    normed_tracks |>
      ggplot(
        aes(F2_z, F1_z)
      ) +
      geom_path(
        aes(
          group = interaction(speaker, id)
        ),
        alpha = 0.3
      ) +
      scale_y_reverse() +
      scale_x_reverse() +
      facet_wrap(~speaker) +
      coord_cartesian(
        xlim = c(3.5, -3.5),
        ylim = c(3.5, -3.5)
      ) +
      theme(
        aspect.ratio = 1
      )