  The intelligibility and quality of speech from a mobile phone or public
announcement system are often affected by background noise in the listening
environment. By pre-processing the speech signal it is possible to improve the
speech intelligibility and quality -- this is known as near-end listening
enhancement (NLE). Although, existing NLE techniques are able to greatly
increase intelligibility in harsh noise environments, in favorable noise
conditions the intelligibility of speech reaches a ceiling where it cannot be
further enhanced. Actually, the focus of existing methods solely on improving
the intelligibility causes unnecessary processing of the speech signal and
leads to speech distortions and quality degradations. In this paper, we provide
a new rationale for NLE, where the target speech is minimally processed in
terms of a processing penalty, provided that a certain performance constraint,
e.g., intelligibility, is satisfied. We present a closed-form solution for the
case where the performance criterion is an intelligibility estimator based on
the approximated speech intelligibility index and the processing penalty is the
mean-square error between the processed and the clean speech. This produces an
NLE method that adapts to changing noise conditions via a simple gain rule by
limiting the processing to the minimum necessary to achieve a desired
intelligibility, while at the same time focusing on quality in favorable noise
situations by minimizing the amount of speech distortions. Through simulation
studies, we show the proposed method attains speech quality on par or better
than existing methods in both objective measurements and subjective listening
tests, whilst still sustaining objective speech intelligibility performance on
par with existing methods.
