Replication Package for AequeVox:Automated Fariness Testing for Speech Recognition Systems

Overview

AequeVox

Replication Package for AequeVox:Automated Fariness Testing for Speech Recognition Systems

README under development.

Python Packages Required

  • numpy
  • scipy
  • math
  • librosa
  • random
  • time
  • json
  • threading
  • re
  • nltk

ASR Specific Packages

Google Cloud

  • speech
  • Storage

Microsoft Azure

  • Azure.cognitiveservices.speech

IBM Cloud

  • ibm_watson
  • ibm_watson.websocket
  • Ibm_cloud_sdk_core.authenticators

The code is separated into 2 sections, Generation and Analysis.

Generation:

transGen.py

  • Lists all transformation types and magnitudes to be used. Can be modified as necessary.
  • Requires the specification of file names of all the original speech files.

Generates transformed speech files with form {Original File Name}{Transformation Type Abbreviation}{Magnitude of Transformation Parameter, theta}.wav

List of Abbreviations.

  1. A - Amplitude
  2. C - Clipping
  3. D - Drop
  4. F - Frame
  5. HP - Highpass
  6. LP - LP
  7. N - Noise
  8. S - Scale

GCP_Recog.py

Requires Google cloud client libraries and associated keys.

Takes a group name and the list of all original files in the group to generate transcripts.

MS_Recog.py

Requires Microsoft Azure client libraries and associated key and region.

Takes a group name and the list of all original files in the group to generate transcripts.

IBM_Recog.py

Requires IBM client libraries and associated key and service URL..

Takes a group name and the list of all original files in the group to generate transcripts.

compASR.py

Takes the names of two ASR systems and group names to generate a distance metric. Result yields text files with distance metrics for specified groups.

Users are requested to use the distance metrics to calculate the D values for each transformation.

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