  Automatic speech recognition (ASR) plays a pivotal role in our daily lives,
offering utility not only for interacting with machines but also for
facilitating communication for individuals with partial or profound hearing
impairments. The process involves receiving the speech signal in analog form,
followed by various signal processing algorithms to make it compatible with
devices of limited capacities, such as cochlear implants (CIs). Unfortunately,
these implants, equipped with a finite number of electrodes, often result in
speech distortion during synthesis. Despite efforts by researchers to enhance
received speech quality using various state-of-the-art (SOTA) signal processing
techniques, challenges persist, especially in scenarios involving multiple
sources of speech, environmental noise, and other adverse conditions. The
advent of new artificial intelligence (AI) methods has ushered in cutting-edge
strategies to address the limitations and difficulties associated with
traditional signal processing techniques dedicated to CIs. This review aims to
comprehensively cover advancements in CI-based ASR and speech enhancement,
among other related aspects. The primary objective is to provide a thorough
overview of metrics and datasets, exploring the capabilities of AI algorithms
in this biomedical field, and summarizing and commenting on the best results
obtained. Additionally, the review will delve into potential applications and
suggest future directions to bridge existing research gaps in this domain.
