Advanced Artificial Intelligence Algorithms in Cochlear Implants: Review
  of Healthcare Strategies, Challenges, and Perspectives
Billel Essaid Hamza Kheddar Noureddine Batel Abderrahmane Lakas Muhammad E. H. Chowdhury
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 either partial or profound
hearing impairments. The process involves receiving the speech signal in
analogue form, followed by various signal processing algorithms to make it
compatible with devices of limited capacity, 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
signal processing techniques, challenges persist, especially in scenarios
involving multiple sources of speech, environmental noise, and other
circumstances. 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 review 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, 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.