lobanov_2() takes a data frame where the first four columns are:
speaker identifiers,
vowel identifiers,
first formant values in Hertz,
second formant values in Hertz.
It returns a dataframe with two additional columns, F1_lob2 and F2_lob2,
containing normalised formant values.
Value
a dataframe matching the input dataframe with additional columns
F1_lob2 and F2_lob2, containing the lobanov normalised F1 and F2 values
respectively.
Details
This functions applies Lobanov 2.0 normalisation presented in Brand et al. (2021). This variant of Lobanov normalisation is designed to work for datasets whether the vowel types have different token counts from one another. The Lobanov 2.0 value for a vowel is given by $$F_{lobanov2.0_i} = \frac{F_{raw_i} - \mu(\mu_{vowel_1}, \ldots, \mu_{vowel_n})}{\sigma(\mu_{vowel_1}, \ldots, \mu_{vowel_n})}$$ where, for ease of notation, we assume all values are from a single speaker. We signify the n vowel types as vowel_1, ..., vowel_2, while i indicates the formant number. We implement the function for F1 and F2.
References
Brand, James, Jen Hay, Lynn Clark, Kevin Watson & Márton Sóskuthy (2021): Systematic co-variation of monophthongs across speakers of New Zealand English. Journal of Phonetics. Elsevier. 88. 101096. doi:10.1016/j.wocn.2021.101096
Examples
normed_vowels <- lobanov_2(onze_vowels)
head(normed_vowels)
#> speaker vowel F1_50 F2_50 speech_rate gender yob word F1_lob2
#> 1 IA_f_065 THOUGHT 514 868 4.3131 F 1891 word_09539 -0.72128947
#> 2 IA_f_065 FLEECE 395 2716 4.3131 F 1891 word_22664 -1.66034667
#> 3 IA_f_065 KIT 653 2413 4.3131 F 1891 word_02705 0.37559246
#> 4 IA_f_065 DRESS 612 2372 4.3131 F 1891 word_23651 0.05205175
#> 5 IA_f_065 GOOSE 445 2037 4.3131 F 1891 word_06222 -1.26578482
#> 6 IA_f_065 GOOSE 443 2258 4.3131 F 1891 word_06222 -1.28156729
#> F2_lob2
#> 1 -1.9212428
#> 2 1.4915434
#> 3 0.9319794
#> 4 0.8562628
#> 5 0.2376030
#> 6 0.6457338