simulatePronunciations: Generate simulated pronunciations

In PeterHendrix13/NDRa: Generate Simulated Naming Latencies and Pronunciations in the NDRa Model

Description

Generate simulated pronunciation for a set of words or nonwords.

Usage

simulatePronunciations(lexicon = lex, weightsSem = weights_sem,
  weightsPhon = weights_phon, parallel = TRUE,
  numCores = detectCores(), verbose = TRUE)

Arguments

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.

References

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.

Examples

# Load data for the ELP simulations in Hendrix (2018)
data(elp)

# Generate simulated pronunciations for a lexicon
elp$SimPron = simulatePronunciations(elp)

PeterHendrix13/NDRa documentation built on May 7, 2019, 6:05 a.m.