accent_rating

This is a collection of scripts and data I used when working on my dissertation.

• recording_procedure.praat – this is the script I used for collecting the audio
  data (it’s a mess like most Praat scripts I’ve written, but it was good enough
  to do what I wanted)

• survey.sur – PsyToolkit survey for collecting metadata and running the
  experiment
• rating_experiment.psy – The experiment itself
• scale_l_to_r.txt – Code that draws a sliding scale in
  rating_experiment.psy with left-to-right orientation
• scale_r_to_l.txt – Code that draws a sliding scale in
  rating_experiment.psy with right-to-left orientation
• sounds.txt – List of audio files used in the experiment (audio files are not
  included due to license limitations)
• bitmaps/ – Collection of pictures used in rating_experiment.psy
• tables/ – Collection of sounds used in rating_experiment.psy. Each table has
  three columns: variable name, file name, duration

• measure_EER.py – script that draws DET curves and calculates EER values from
  the sid_results_table_individual_files_filtered.csv results table. Simply
  run python measure_EER.py. The script creates a folder named
  EER_measurements which contains temporary files to verify that same and
  different speaker labels are correct.
• sid_results_table_individual_files_filtered.csv – different-sex recordings
  have been filtered out

• phonexia_lid_wrapper.sh – wrapper for the Phonexia LID technology (this is
  mainly here to show which subset of languages was used in the analysis)
• speechbrain_lid_wrapper.py – wrapper for the SpeechBrain LID technology it
  subclasses the EncoderClassifier to extract scores for English and Czech apart
  from the best-matching language (the audio files are not included due to
  license limitations so the script does not really produce the results!)

• sid4_wrapper.sh – wrapper for the Phonexia SID4 technology. A simple helper
  script which extracts voiceprints from all wave files in one directory and
  then compares all voiceprints with each other. It also saves the amount of
  “net speech” in each voiceprint to a file.
• speechbrain_sid_wrapper.py – a wrapper for SpeechBrain’s SpeakerRecognition.
  It has options for computing embeddings from audio files or loading previously
  created embeddings. It subclasses SpeakerRecognition in order to be able to
  perform verification on embeddings instead of on audio files.
  The script creates embeddings from all audio files in a directory and then
  performs speaker verification either on all combinations or all permutations
  of the embeddings set.