SMNG Code: Quick Start Guide

Data collection

Data collection for compensation-style experiment

Unless otherwise stated, any code referenced is in the carrien/free-speech repository on GitHub: https://github.com/carrien/free-speech

For data collection of a compensation experiment, run the function templates/modelExpt/run_modelComp_expt.m. That is our "wrapper" function that sets up a file expt.mat. At the end, it calls a function templates/modelExpt/run_modelComp_audapter.m. That is our "engine" function that actually runs Audapter and goes through all of the trials of the experiment. That's the basics, at least.

The results of these functions are two primary files: expt.mat, which stores metadata, and data.mat, which stores the audio recordings and other Audapter output.

Data collection for adaptation-style experiment

Essentially the same as for compensation-style experiment, but the functions are templates/modelExpt/run_modelExpt_expt.m and templates/modelExpt/run_modelExpt_audapter.m. 

Data analysis

After data collection, there are two main methods of extracting the relevant formant data. Both result in a file called dataVals.mat, which is nicely formatted for further analysis.

Quick n' dirty data analysis method

In MATLAB, navigate to the folder containing the expt.mat and data.mat files, then run gen_dataVals_from_audapterdata (which comes from the free-speech repo). It will generate a file dataVals_audapter.mat using the pitch and formant data provided by Audapter. Since Audapter's pitch and formant data is calculated in real time as Audapter is running, it isn't super accurate and doesn't have the benefit of looking at the trial holistically, like Praat would. In our lab, we typically trust Audapter's formant data as a first pass after data collection or when working with pilot data. We don't use Audapter's formant data for publication-quality reports.

More reliable and time-intensive data analysis method

Our typical pipeline for data analysis includes several steps using various tools. It uses Praat to find formant values and allows you to configure parameters during formant analysis. That whole process is documented here. At the end of that process, you will have a file dataVals.mat.

Understanding dataVals

More info about dataVals is here: Basic dataVals structure. This is particularly useful info if you want to make your own plotting or analysis scripts.

Plotting

For plotting and further data analysis, a file fmtMatrix.mat is useful and can be derived from dataVals.mat. Use the wrapper templates/modelExpt/gen_fmtMatrix_modelComp.m to generate fmtMatrix.mat. 

Then, see resources in the free-speech/plotting/ folder, but the most simple is plotting/plot_fmtMatrix.m. If you are in the data folder in Matlab, and your fmtMatrix file is called "fmtMatrix.mat", you can run it with plot_fmtMatrix([], 'fmtMatrix.mat'). 

Other resources