Description Usage Arguments References Examples
Generate simulated pronunciation for a set of words or nonwords.
1 2 3 | simulatePronunciations(lexicon = lex, weightsSem = weights_sem,
weightsPhon = weights_phon, parallel = TRUE,
numCores = detectCores(), verbose = TRUE)
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lexicon |
A dataframe with the colums "Word" and "Gestures". "Gestures" are demi-syllables (see Klatt, 1979) and can be generated using gestures(). |
weightsSem |
An orthography-to-semantics weight matrix with letter unigrams and bigrams as cues and words as outcomes. The default, "weights_sem" uses the weight matrix from Hendrix et al. (2018). |
weightsPhon |
A phonology-to-semantic weight matrix with demi-syllables as cues and words as outcomes. The default, "weigths_phon" uses the weight matrix from Hendrix et al. (2018). |
parallel |
Should computations be carried out in parallel? Defaults to TRUE. |
numCores |
The number of cores to use for parallel computation. By default all available cores are used. |
Hendrix, P, Ramscar, M., & Baayen, R. H. (2019). NDRa: a single route model of response times in the reading aloud task based on discriminative learning. Manuscript.
Klatt, D. H. (1979). Speech perception: a model of acoustic-phonetic analysis and lexical access. Journal of Phonetics, 7, 279-312.
1 2 3 4 5 | # Load data for the ELP simulations in Hendrix (2018)
data(elp)
# Generate simulated pronunciations for a lexicon
elp$SimPron = simulatePronunciations(elp)
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