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Speech perception is key to verbal communication. For people with hearingloss, the capability to recognize speech is restricted, particularly in a noisyenvironment or the situations without visual cues, such as lip-readingunavailable via phone call. This study aimed to understand the improvement ofvocoded speech intelligibility in cochlear implant (CI) simulation through twopotential methods: Speech Enhancement (SE) and Audiovisual Integration. A fullyconvolutional neural network (FCN) using an intelligibility-oriented objectivefunction was recently proposed and proven to effectively facilitate the speechintelligibility as an advanced denoising SE approach. Furthermore, audiovisualintegration is reported to supply better speech comprehension compared toaudio-only information. An experiment was designed to test speechintelligibility using tone-vocoded speech in CI simulation with a group ofnormal-hearing listeners. Experimental results confirmed the effectiveness ofthe FCN-based denoising SE and audiovisual integration on vocoded speech. Also,it positively recommended that these two methods could become a blended featurein a CI processor to improve the speech intelligibility for CI users undernoisy conditions.

A Study of Joint Effect on Denoising Techniques and Visual Cues to Improve Speech Intelligibility in Cochlear Implant Simulation