  The estimation of speech intelligibility is still far from being a solved
problem. Especially one aspect is problematic: most of the standard models
require a clean reference signal in order to estimate intelligibility. This is
an issue of some significance, as a reference signal is often unavailable in
practice. In this work, therefore a non-intrusive speech intelligibility
estimation framework is presented. In it, human listeners' performance in
keyword recognition tasks is predicted using intelligibility measures that are
derived from models trained for automatic speech recognition (ASR). One such
ASR-based and one signal-based measure are combined into a full framework, the
proposed NO-Reference Intelligibility (Nori) estimator, which is evaluated in
predicting the performance of both normal-hearing and hearing-impaired
listeners in multiple noise conditions. It is shown that the Nori framework
even outperforms the widely used reference-based (or intrusive) short-term
objective intelligibility (STOI) measure in most considered scenarios, while
being applicable in fully blind scenarios with no reference signal or
transcription, creating perspectives for online and personalized optimization
of speech enhancement systems.
