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Artificial Intelligence for Cochlear Implants: Review of Strategies, Challenges, and Perspectives

Billel Essaid, Hamza Kheddar, Noureddine Batel, Muhammad E. H. Chowdhury, Abderrahmane Lakas

TL;DR

This paper surveys how AI methods—across ML, DL, RL, CNNs, GANs, RNNs/LSTMs, and autoencoders—are applied to cochlear implants to improve automatic speech recognition and speech enhancement under challenging acoustic conditions. It systematically catalogs datasets and evaluation metrics, presents a taxonomy of CI-centered AI approaches (from AI-driven fitting with FOX to imaging, segmentation, and thresholding), and highlights major applications in speech denoising, imaging quality, and electrode localization. The review also identifies open issues—such as real-time processing on resource-constrained devices, data privacy, and model transparency—and articulates future directions including transformers, deep transfer learning, federated learning, and multi-modal integration to advance CI performance and user quality of life. Overall, the work provides a consolidated roadmap for researchers and clinicians to leverage AI for more accurate ASR, better speech intelligibility, and improved image-guided CI programming and planning.

Abstract

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.

Artificial Intelligence for Cochlear Implants: Review of Strategies, Challenges, and Perspectives

TL;DR

This paper surveys how AI methods—across ML, DL, RL, CNNs, GANs, RNNs/LSTMs, and autoencoders—are applied to cochlear implants to improve automatic speech recognition and speech enhancement under challenging acoustic conditions. It systematically catalogs datasets and evaluation metrics, presents a taxonomy of CI-centered AI approaches (from AI-driven fitting with FOX to imaging, segmentation, and thresholding), and highlights major applications in speech denoising, imaging quality, and electrode localization. The review also identifies open issues—such as real-time processing on resource-constrained devices, data privacy, and model transparency—and articulates future directions including transformers, deep transfer learning, federated learning, and multi-modal integration to advance CI performance and user quality of life. Overall, the work provides a consolidated roadmap for researchers and clinicians to leverage AI for more accurate ASR, better speech intelligibility, and improved image-guided CI programming and planning.

Abstract

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.
Paper Structure (25 sections, 4 equations, 12 figures, 5 tables)

This paper contains 25 sections, 4 equations, 12 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Statistics on investigated papers
  4. Motivation and contribution
  5. Background in speech processing for CI
  6. Speech processing for CI
  7. Datasets
  8. Metrics
  9. Taxonomy of CI-based AI techniques
  10. CI-based AI Implementation
  11. ML-based methods
  12. CNN-based methods
  13. GAN-based methods
  14. RNN/LSTM-based methods
  15. AE-based methods
  16. ...and 10 more sections

Figures (12)

  • Figure 1: Top keywords in AI-based CI research field.
  • Figure 2: Bibliometrics analysis of the papers included in this review. (a) Papers distribution over the last years. (b) Percentage breakdown of paper types included in this review.
  • Figure 3: Illustration of a CI depicting the components situated externally and internally within the device macherey2014cochlear
  • Figure 4: Taxonomy of the employed AI techniques for CI.
  • Figure 5: The fundamental operating concept of the FOX involves inputting an initial program and multiple psychoacoustic test outcomes. FOX processes this information and generates fitting suggestions as its output. When integrated with proprietary outcome and CI fitting software, the shaded boxes represent its functionality, while the unfilled boxes represent its standalone capability govaerts2010development. Audiqueen is a dataset with A and E (A&E) phoneme discrimination.
  • ...and 7 more figures