  The intelligibility of natural speech is seriously degraded when exposed to
adverse noisy environments. In this work, we propose a deep learning-based
speech modification method to compensate for the intelligibility loss, with the
constraint that the root mean square (RMS) level and duration of the speech
signal are maintained before and after modifications. Specifically, we utilize
an iMetricGAN approach to optimize the speech intelligibility metrics with
generative adversarial networks (GANs). Experimental results show that the
proposed iMetricGAN outperforms conventional state-of-the-art algorithms in
terms of objective measures, i.e., speech intelligibility in bits (SIIB) and
extended short-time objective intelligibility (ESTOI), under a Cafeteria noise
condition. In addition, formal listening tests reveal significant
intelligibility gains when both noise and reverberation exist.
