Package: abcmrt16
ABC-MRT16 Python PackageCode for running articulation band correlation - modified rhyme tests (ABC-MRT).Building and Installing the Package LocallyTo install the package run the following:pip install abcmrt16Alternatively, to build and install the package, clone this repository and run the following
from the root of the git repository :pip install .BackgroundThis software implements the ABC-MRT16 algorithm for objective estimation of
speech intelligibility.  The algorithm is discussed in detail in [1] and [2].
ABC-MRT is short for “Articulation Band Correlation Modified Rhyme Test.” The
software was  originally written by NTIA/ITS in MATLAB
(https://github.com/NTIA/ABC-MRT16), but has now been ported to Python.The Modified Rhyme Test (MRT) [3] is a protocol for evaluating speech
intelligibility using human subjects. The subjects are presented with the
task of identifying one of six different words that take the phonetic form
CVC.  The six options differ only in the leading or trailing consonant.
MRT results take the form of success rates (corrected for guessing) that
range from 0 (guessing) to 1 (correct identification in every case).These success rates form a measure of speech intelligibility in this
specific (MRT) context.Articulation Band Correlation-MRT (ABC-MRT) is a signal processing
algorithm that processes MRT audio files and produces success rates.  The
goal of ABC-MRT is to produce success rates that agree with those produced
by MRT. Thus ABC-MRT is an automated or objective version of MRT and no
human subjects are required.ABC-MRT performs a narrowband (nominally 4 kHz) analysis. ABC-MRT16
is applicable to narrowband, wideband, superwideband, and fullband speech.
ABC-MRT processes the first 17 AI bands while ABC-MRT16 processes all 20 AI
bands, as well as an additional "AI Band 21" that covers 7 kHz to 20 kHz.
Of equal importance is that ABC-MRT16 incorporates a model for attention
that allows it to properly operate across the different bandwidths without
any bandwidth detection or switching.Unless backwards compatibility is required, ABC-MRT16 is the recommended
algorithm, even if only narrowband conditions are to be tested. The
attention model makes it superior to ABC-MRT. ABC-MRT16 is the only algorithm
that has been ported to Python.The software provided here runs using the Python interpreter.Application of ABC-MRT(16) to a speech communication system-under-test (SUT)
requires two steps.Pass a set of reference recordings through the SUT to produce a set
of test recordings.Apply ABC-MRT(16) to the test recordings to produce a success rates that
describe the intelligibility of the SUT.All of these steps are run by the intelligibility measurement software found athttps://github.com/usnistgov/intelligibility. Although it is possible to use
stand alone, the GUI available athttps://github.com/usnistgov/mcvqoeis also
recommended.References[1] S. Voran "Using articulation index band correlations to objectively
estimate speech intelligibility consistent with the modified rhyme test,"
Proc. 2013 IEEE Workshop on Applications of Signal Processing to Audio and
Acoustics, New Paltz, NY, October 20- 23, 2013.  Available atwww.its.bldrdoc.gov/audioafter October 20, 2013.[2] S. Voran "A multiple bandwidth objective speech intelligibility
estimator based on articulation index band correlations and attention,"
Proc. 2017 IEEE International Conference on Acoustics, Speech, and
Signal Processing, New Orleans, March 5-9, 2017.  Available atwww.its.bldrdoc.gov/audio.[3] ANSI S3.2, "American national standard method for measuring the
intelligibility of speech over communication systems," 1989.LegalNTIATHE NATIONAL TELECOMMUNICATIONS AND INFORMATION ADMINISTRATION, INSTITUTE
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