Durational measurements on the Dutch prefix ont-

Description

Durational measurements on the Dutch prefix ont- in the Spoken Dutch Corpus.

Usage

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Format

A data frame with 102 observations on the following 11 variables.

Word

a factor with the words as levels.

Frequency

a numeric vector with the word's logarithmically transformed frequency in the Spoken Dutch Corpus.

Speaker

a factor with speakers as levels.

Sex

a factor with levels female and male.

YearOfBirth

a numeric vector coding year of birth of the speaker - 1900.

DurationOfPrefix

a numeric vector for the duration of ont- in seconds

DurationPrefixVowel

a numeric vector for the duration of the vowel in the prefix in seconds.

DurationPrefixNasal

a numeric vector for the duration of the nasal in the prefix in seconds.

DurationPrefixPlosive

a numeric vector for the duration of the plosive in the prefix in seconds.

NumberOfSegmentsOnset

a numeric vector for the number of segments in the onset of the stem.

PlosivePresent

a factor with levels no and yes for whether the plosive is realized in the signal.

SpeechRate

a numeric vector coding speech rate in number of syllables per second.

References

Pluymaekers, M., Ernestus, M. and Baayen, R. H. (2005) Frequency and acoustic length: the case of derivational affixes in Dutch, Journal of the Acoustical Society of America, 118, 2561-2569.

Examples

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data(durationsOnt)

###### modeling the duration of the prefix

prefix.lm = lm(DurationOfPrefix ~ (YearOfBirth + SpeechRate) * Frequency, 
   data = durationsOnt)
summary(prefix.lm)

# ---- model criticism

plot(prefix.lm)
outliers = c(36, 35, 17, 72)
prefix.lm = lm(DurationOfPrefix ~ (YearOfBirth + SpeechRate) * Frequency, 
   data = durationsOnt[-outliers,])
summary(prefix.lm)

###### modeling the presence of the /t/ 

library(rms)
durationsOnt.dd = datadist(durationsOnt)
options(datadist = 'durationsOnt.dd')

plosive.lrm = lrm(PlosivePresent ~ SpeechRate + YearOfBirth, 
   data = durationsOnt, x = TRUE, y = TRUE)
plosive.lrm
validate(plosive.lrm, bw = TRUE, B = 200)

###### modeling the duration of the /n/

nasal.lm = lm(DurationPrefixNasal ~ PlosivePresent + Frequency + 
   YearOfBirth, data = durationsOnt)
summary(nasal.lm)

# ---- model criticism

plot(nasal.lm)
outliers = c(71, 28, 62, 33)
nasal.lm = lm(DurationPrefixNasal ~ PlosivePresent + Frequency + 
   YearOfBirth, data = durationsOnt[-outliers,])
summary(nasal.lm)

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