Advancements in generative algorithms promise new heights in what can be achieved, for example, in the speech enhancement domain. Beyond the ubiquitous noise reduction, destroyed speech components can now be restored—something not previously achievable. These emerging advancements create both opportunities and risks, as speech intelligibility can be impacted in a multitude of beneficial and detrimental ways. As such, there exists a need for methods, materials and tools for enabling rapid and effective assessment of speech intelligibility.
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