  Speech enhancement (SE) methods mainly focus on recovering clean speech from
noisy input. In real-world speech communication, however, noises often exist in
not only speaker but also listener environments. Although SE methods can
suppress the noise contained in the speaker's voice, they cannot deal with the
noise that is physically present in the listener side. To address such a
complicated but common scenario, we investigate a deep learning-based joint
framework integrating noise reduction (NR) with listening enhancement (LE), in
which the NR module first suppresses noise and the LE module then modifies the
denoised speech, i.e., the output of the NR module, to further improve speech
intelligibility. The enhanced speech can thus be less noisy and more
intelligible for listeners. Experimental results show that our proposed method
achieves promising results and significantly outperforms the disjoint
processing methods in terms of various speech evaluation metrics.
