The goal of syllabify is to provide tidy syllabification of phonetic transcriptions. So far, only CMU dict transcriptions are supported.
I’ve largely utilized the same approach as Kyle Gorman’s python implementation
syllabifyr is now on Cran
You can also install syllabifyr from github with:
library(syllabifyr)
syllabify("AO0 S T R EY1 L Y AH0")
#> # A tibble: 8 x 4
#> syll part phone stress
#> <dbl> <chr> <chr> <chr>
#> 1 1 nucleus AO 0
#> 2 2 onset S 1
#> 3 2 onset T 1
#> 4 2 onset R 1
#> 5 2 nucleus EY 1
#> 6 2 coda L 1
#> 7 3 onset Y 0
#> 8 3 nucleus AH 0
syllabify(c("AO0", "S", "T", "R", "EY1", "L", "Y", "AH0"))
#> # A tibble: 8 x 4
#> syll part phone stress
#> <dbl> <chr> <chr> <chr>
#> 1 1 nucleus AO 0
#> 2 2 onset S 1
#> 3 2 onset T 1
#> 4 2 onset R 1
#> 5 2 nucleus EY 1
#> 6 2 coda L 1
#> 7 3 onset Y 0
#> 8 3 nucleus AH 0library(tidyverse)
syllabficiation <- tribble(~word, ~transcription,
"Alaska", "AH0 L AE1 S K AH0",
"constraint", "K AH0 N S T R EY1 N T",
"canyon", "K AE1 N Y AH0 N",
"value", "V AE1 L Y UW0")%>%
mutate(syllable_df = map(transcription, syllabify))
syllabficiation$syllable_df[[1]]
#> # A tibble: 6 x 4
#> syll part phone stress
#> <dbl> <chr> <chr> <chr>
#> 1 1 nucleus AH 0
#> 2 2 onset L 1
#> 3 2 nucleus AE 1
#> 4 2 coda S 1
#> 5 3 onset K 0
#> 6 3 nucleus AH 0
syllabficiation$syllable_df[[2]]
#> # A tibble: 9 x 4
#> syll part phone stress
#> <dbl> <chr> <chr> <chr>
#> 1 1 onset K 0
#> 2 1 nucleus AH 0
#> 3 1 coda N 0
#> 4 2 onset S 1
#> 5 2 onset T 1
#> 6 2 onset R 1
#> 7 2 nucleus EY 1
#> 8 2 coda N 1
#> 9 2 coda T 1
syllabficiation$syllable_df[[3]]
#> # A tibble: 6 x 4
#> syll part phone stress
#> <dbl> <chr> <chr> <chr>
#> 1 1 onset K 1
#> 2 1 nucleus AE 1
#> 3 1 coda N 1
#> 4 2 onset Y 0
#> 5 2 nucleus AH 0
#> 6 2 coda N 0
syllabficiation$syllable_df[[4]]
#> # A tibble: 5 x 4
#> syll part phone stress
#> <dbl> <chr> <chr> <chr>
#> 1 1 onset V 1
#> 2 1 nucleus AE 1
#> 3 1 coda L 1
#> 4 2 onset Y 0
#> 5 2 nucleus UW 0