This repository describes the scripts from the paper Ten Oever & Martin (2021), An oscillating computational model can track pseudo-rhythmic speech by using linguistic predictions, Elife, 10:e68066. DOI: https://doi.org/10.7554/eLife.68066
The repository consists of scripts belonging to the Corpus Gesproken Nederlands (CGN), simulations and fitting with STiMCON
CGN_Fig2.py:
This script extracts and plot the basic temporal variation in the syllables and words of the CGN related to Figure 2 of the main manuscript.
CGN_Tab1_Fig3_Fig7.py:
The ordinary least square and related figures.
RNN_Model.py:
The RNN model
RNN_subFun.py:
Subfunctions to use the RNN_Model
STiMCON_Fig4_Fig5_Fig8A.py
Shows the basic behavior of STiMCON (Figure 4), the threshold/timing of activation (Figure 5) and ambiguous daga overall simulations (Figure 8A)
STiMCON_Fig6.py
Shows how acoustic time and model time is not the same in STiMCON (Figure 6)
STiMCON_Fig8C.py
Fitting of the da/ga data using the first active node as output (Figure 8C)
STiMCON_Fig8D.py
Fitting of the da/ga data using the relative node activation as output (Figure 8D)
STiMCON_core.py
Core script for the STiMCON model which has all the low-level code
STiMCON_plot.py
Plotting output of the STiMCON
STiMCON_sen.py
Creating sensory input going into the STiMCON