mlsanityData to 7 Oct 2026

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

arXiv 2403.15442 ↗First posted 17 Mar 2024eess.AS cs.AI cs.CVcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 13 of its 110 references that are in the map. First posted on arXiv 17 Mar 2024.

Builds on

13 of 110 references.

References · 13

  1. 2015LSTM: A Search Space Odysseycited by 315
  2. 2024Automatic Speech Recognition using Advanced Deep Learning Approaches: A surveycited by 50
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  6. 2023AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directionscited by 18
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  10. 2021Inner-ear Augmented Metal Artifact Reduction with Simulation-based 3D Generative Adversarial Networkscited by 4
  11. 2020A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applicationscited by 3
  12. 2020Clinical Micro-CT Empowered by Interior Tomography, Robotic Scanning, and Deep Learningcited by 1
1 more
  1. 2023A Fused Deep Denoising Sound Coding Strategy for Bilateral Cochlear Implantscited by 1

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Cited by year

Citing papers per year: 2024: 1; 2025: 3.

4 papers.

  1. 2024Automatic Speech Recognition with BERT and CTC Transformers: A Reviewcited by 8
  2. 2025Improving Pretrained YAMNet for Enhanced Speech Command Detection via Transfer Learningcited by 1
  3. 2025Enhancing Cochlear Implant Signal Coding with Scaled Dot-Product Attentioncited by 1
  4. 2025Transfer Learning-Based Deep Residual Learning for Speech Recognition in Clean and Noisy Environmentscited by 0