| Despite their inherent limitations, all of the machine testing
methods that weve discussed can show good agreement if the
system under test is reasonably well behaved. But intelligibility
testing is most consequential (and potentially most useful) when
the system has problems severe enough to impair speech transmission.
Such problems can arise from a variety of sources and conditions,
many of which can fool any of the machine testing methods.
Contemporary sound systems are sophisticated complexes of diverse,
interacting components. As this simplified diagram illustrates,
they invariably include signal processing elements whose effects
on intelligibility, and on the instruments designed to measure
it, may be difficult to predict. While the consequences of relatively
simple analog processing (such as equalization and limiting)
generally are benign, the same may not be true of new, powerful
digital signal processing technologies.
For example, much attention is now focused upon using DSPs
to deconvolve the response of a space in order to
suppress echoes and subtract or add reverberation. Because the
algorithms that are involved affect the time order of the signal,
there may be large consequences if these devices are misadjusted.
Furthermore, if speakers are repositioned, or the acoustics of
the space changes (when a curtain is closed, for example), then
the particular deconvolution likely will no longer be valid and
may, in fact, cause very destructive effects.
None of the present machine measures for intelligibility accounts
for time distortions. In fact, we could conceive of a hypothetical
system that reversed the time aspect of a signal, like playing
a tape backward: no machine method would show any decrease in
the intelligibility score for such a system, though it would
obviously render speech unintelligible.
Whats needed is an analyzer thats sufficiently smart to
detect all of the factors which affect intelligibility, and render
a conclusive judgement, without relying heavily on the operators
interpretation. But the unavoidable truth is that, as sophisticated
as machine-based measurement systems may be, they cannot yet
approach the complexity of the human ear/brain mechanism informed
by a lifetime of experience decoding speech. We can only model
those aspects of that exquisitely fine-tuned mechanism that we
have come to understand. The many remaining questions regarding
how it works and what factors may affect it can only be answered
by further research.
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Appendices
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