  The intelligibility of speech severely degrades in the presence of
environmental noise and reverberation. In this paper, we propose a novel deep
learning based system for modifying the speech signal to increase its
intelligibility under the equal-power constraint, i.e., signal power before and
after modification must be the same. To achieve this, we use generative
adversarial networks (GANs) to obtain time-frequency dependent amplification
factors, which are then applied to the input raw speech to reallocate the
speech energy. Instead of optimizing only a single, simple metric, we train a
deep neural network (DNN) model to simultaneously optimize multiple advanced
speech metrics, including both intelligibility- and quality-related ones, which
results in notable improvements in performance and robustness. Our system can
not only work in non-realtime mode for offline audio playback but also support
practical real-time speech applications. Experimental results using both
objective measurements and subjective listening tests indicate that the
proposed system significantly outperforms state-ofthe-art baseline systems
under various noisy and reverberant listening conditions.
