Normalize vowel formant measurements a log-means normalization procedure as described in Barreda & Nearey (2018). This function is intended to be used within a tidyverse pipeline.
joeyr_norm_logmeans(
.df,
.formant_cols,
.speaker_col,
.vowel_col,
.return = "data",
i_know_more_than_you = FALSE
)
norm_logmeans(.df)The data frame containing the formant measurements you want to normalize. Formant data must be log transformed! See example code below.
The (unquoted) name(s) of the column containing the formant measurements.
The (unquoted) name of the column containing the unique identifiers per speaker.
The (unquoted) name of the column containing the unique identifiers per vowel
A string. By default, "data", which will returned the
your original data with the normalized data appended. If you set this to
"params", you'll get a data frame with the normalization paramters
for each speakers.
Logical. The function won't work if you've got data that doesn't look like log10-transformed formant data. If you want to force the function to run anyway, set this to `TRUE`.
The original dataframe with new columns containing the normalized measurements. These new columns have "_norm" appended to the column names.
The data should not be grouped beforehand (e.g. with group_by).
The data must be numeric, and there cannot be any NAs.
As of June 18, 2025, the `norm_logmeans` function is depreciated in favor of the `norm_nearey` function in the tidynorm package. I recommend you switch to that for future code. If you want to retain the current functionality, you can call `joeyr_norm_logmeans` instead.
Thanks to Santiago Barreda for providing most of the code for this function.
Barreda, Santiago, and Terrance M. Nearey. 2018. "A Regression Approach to Vowel Normalization for Missing and Unbalanced Data." The Journal of the Acoustical Society of America 144(1): 500–520. https://doi.org/10.1121/1.5047742.
library(tidyverse)
idaho <- joeysvowels::idahoans
#> Error in loadNamespace(x): there is no package called ‘joeysvowels’
# Basic usage. Note that the data has to be log10-transformed.
idaho %>%
mutate(F1_log = log10(F1), F2_log = log10(F2)) %>%
norm_logmeans(.formant_cols = c(F1_log, F2_log),
.speaker_col = speaker,
.vowel_col = vowel) %>%
head()
#> Error in norm_logmeans(., .formant_cols = c(F1_log, F2_log), .speaker_col = speaker, .vowel_col = vowel): unused arguments (.formant_cols = c(F1_log, F2_log), .speaker_col = speaker, .vowel_col = vowel)
# Return the speaker paramters instead.
idaho %>%
mutate(F1_log = log10(F1), F2_log = log10(F2)) %>%
norm_logmeans(.formant_cols = c(F1_log, F2_log),
.speaker_col = speaker,
.vowel_col = vowel,
.return = "params") %>%
head()
#> Error in norm_logmeans(., .formant_cols = c(F1_log, F2_log), .speaker_col = speaker, .vowel_col = vowel, .return = "params"): unused arguments (.formant_cols = c(F1_log, F2_log), .speaker_col = speaker, .vowel_col = vowel, .return = "params")
# If you forget to log-transform the data, it'll throw an error.
idaho %>%
norm_logmeans(.formant_cols = c(F1, F2),
.speaker_col = speaker,
.vowel_col = vowel)
#> Error in norm_logmeans(., .formant_cols = c(F1, F2), .speaker_col = speaker, .vowel_col = vowel): unused arguments (.formant_cols = c(F1, F2), .speaker_col = speaker, .vowel_col = vowel)
# But you can force the function to run on non-transformed data if you're sure you know what you're doing.
idaho %>%
norm_logmeans(.formant_cols = c(F1, F2),
.speaker_col = speaker,
.vowel_col = vowel,
i_know_more_than_you = TRUE) %>%
head()
#> Error in norm_logmeans(., .formant_cols = c(F1, F2), .speaker_col = speaker, .vowel_col = vowel, i_know_more_than_you = TRUE): unused arguments (.formant_cols = c(F1, F2), .speaker_col = speaker, .vowel_col = vowel, i_know_more_than_you = TRUE)