  Speech perception is key to verbal communication. For people with hearing
loss, the capability to recognize speech is restricted, particularly in a noisy
environment or the situations without visual cues, such as lip-reading
unavailable via phone call. This study aimed to understand the improvement of
vocoded speech intelligibility in cochlear implant (CI) simulation through two
potential methods: Speech Enhancement (SE) and Audiovisual Integration. A fully
convolutional neural network (FCN) using an intelligibility-oriented objective
function was recently proposed and proven to effectively facilitate the speech
intelligibility as an advanced denoising SE approach. Furthermore, audiovisual
integration is reported to supply better speech comprehension compared to
audio-only information. An experiment was designed to test speech
intelligibility using tone-vocoded speech in CI simulation with a group of
normal-hearing listeners. Experimental results confirmed the effectiveness of
the FCN-based denoising SE and audiovisual integration on vocoded speech. Also,
it positively recommended that these two methods could become a blended feature
in a CI processor to improve the speech intelligibility for CI users under
noisy conditions.
