This package includes a variety of helper functions involving praat,
textgrids, pitchtiers, and intonation-related data wrangling. This has
been written for my own research, but others may find it helpful as
well. This package extends the rPraat package.
You can install the development version of sosprosody like so:
library(devtools)
devtools::install_github('tsostarics/sosprosody')This package provides format and print methods for TextGrid and
PitchTier objects loaded with the respective read functions from
{rPraat}
library(rPraat)
library(sosprosody)
pitchtier <- pt.read("grandmother_LHH_003.PitchTier")
textgrid <- tg.read("grandmother_LHH_003.TextGrid")
pitchtier
#> grandmother_LHH_003.PitchTier: 106 total pitch pulses.
#> 181| |
#> | OOO |
#> | OOOO |
#> | OO |
#> | OOO |
#> | OOO |
#> | OOO |
#> | OOOOOOOOOOOOOOOO OOOOOO |
#> | OOO OOOOOOOO |
#> 58| O |
#> 0 1.6
textgrid
#> grandmother_LHH_003.TextGrid
#> [ words: 4/6 labeled intervals from 0 to 1.6 ]
#> [ phones: 15/17 labeled intervals from 0 to 1.6 ]Here’s an example of processing all the textgrids and pitchtiers in a directory into dataframe representations.
nuclear_words <- "grandmother"
tg_df <- batch_process_textgrids("./")
#> Processed 2 TextGrids
pt_df <- batch_process_pitchtiers("./")
#> Processed 2 PitchTiers
nuclear_regions <- get_nuclear_textgrids(tg_df, nuclear_words)
str(tg_df)
#> 'data.frame': 30 obs. of 9 variables:
#> $ file : chr "grandmother_LHH_003" "grandmother_LHH_003" "grandmother_LHH_003" "grandmother_LHH_003" ...
#> $ word_start : num 0.14 0.14 0.14 0.14 0.44 0.5 0.57 0.57 0.57 0.57 ...
#> $ word_end : num 0.44 0.44 0.44 0.44 0.5 0.57 1.33 1.33 1.33 1.33 ...
#> $ word_label : chr "laura" "laura" "laura" "laura" ...
#> $ word_i : int 1 1 1 1 2 3 4 4 4 4 ...
#> $ phone_start: num 0.14 0.26 0.27 0.39 0.44 0.5 0.57 0.63 0.72 0.84 ...
#> $ phone_end : num 0.26 0.27 0.39 0.44 0.5 0.57 0.63 0.72 0.84 0.89 ...
#> $ phone_label: chr "l" "ɒ" "ɹ" "ə" ...
#> $ phone_i : int 1 2 3 4 5 6 7 8 9 10 ...
#> - attr(*, "tiertype")= chr "interval"
str(pt_df)
#> 'data.frame': 245 obs. of 6 variables:
#> $ file : chr "grandmother_LHH_003" "grandmother_LHH_003" "grandmother_LHH_003" "grandmother_LHH_003" ...
#> $ timepoint : num 0.227 0.237 0.247 0.257 0.267 ...
#> $ hz : num 80.5 82.4 83 82.6 82.3 ...
#> $ semitone_difference: num -0.3946 0 0.1251 0.0499 -0.0256 ...
#> $ semitones_from : num 82.4 82.4 82.4 82.4 82.4 ...
#> $ erb : num 2.49 2.54 2.56 2.55 2.54 ...We can then do some common preprocessing steps, such as coding the nuclear word in the phrase, applying running median smoothing, and normalizing the timepoints.
processed_pt_df <- preprocess_pitchtracks(pt_df,
nuclear_df = nuclear_regions,
runmed_k = 5,
time_normalize = TRUE,
.fromzero = TRUE)The processed pitch tier data frame can then be plotted like so:
library(ggplot2)
processed_pt_df |>
ggplot(aes(x = timepoint_norm,
y = hz_runmed,
color = is_nuclear,
group = file)) +
geom_point() +
geom_line() +
# The rest is just for visuals
scale_color_brewer(palette = 'Dark2') +
theme_minimal() +
coord_fixed(ratio = 1/180) +
theme(legend.position = 'none') +
annotate(geom = "label", x = .9, y = 85, label = "L*+HLL") +
annotate(geom = "label", x = .75, y = 150, label = "LHH")