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

arXiv: https://arxiv.org/abs/2403.15442 · HTML: https://mlsanity.com/paper/2403.15442/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 110 references that are in the map.
- First posted on arXiv 17 Mar 2024.

## Builds on (most cited first, 13 of 13)

- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey](https://mlsanity.com/paper/2403.01255/) (2024)
- [Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools Segmentation](https://mlsanity.com/paper/2203.15177/) (2022)
- [Federated Learning for Computer Vision](https://mlsanity.com/paper/2308.13558/) (2023)
- [Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review](https://mlsanity.com/paper/2304.10550/) (2023)
- [AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions](https://mlsanity.com/paper/2308.13592/) (2023)
- [Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directions](https://mlsanity.com/paper/2308.04522/) (2023)
- [i3PosNet: Instrument Pose Estimation from X-Ray in temporal bone surgery](https://mlsanity.com/paper/1802.09575/) (2018)
- [Optimizing αμ](https://mlsanity.com/paper/2101.12639/) (2021)
- [Inner-ear Augmented Metal Artifact Reduction with Simulation-based 3D Generative Adversarial Networks](https://mlsanity.com/paper/2104.12510/) (2021)
- [A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications](https://mlsanity.com/paper/2004.14832/) (2020)
- [Clinical Micro-CT Empowered by Interior Tomography, Robotic Scanning, and Deep Learning](https://mlsanity.com/paper/2011.08297/) (2020)
- [A Fused Deep Denoising Sound Coding Strategy for Bilateral Cochlear Implants](https://mlsanity.com/paper/2310.01122/) (2023)

## Built on by (most cited first, 4 of 4)

- [Automatic Speech Recognition with BERT and CTC Transformers: A Review](https://mlsanity.com/paper/2410.09456/) (2024)
- [Improving Pretrained YAMNet for Enhanced Speech Command Detection via Transfer Learning](https://mlsanity.com/paper/2504.19030/) (2025)
- [Enhancing Cochlear Implant Signal Coding with Scaled Dot-Product Attention](https://mlsanity.com/paper/2504.19046/) (2025)
- [Transfer Learning-Based Deep Residual Learning for Speech Recognition in Clean and Noisy Environments](https://mlsanity.com/paper/2505.01632/) (2025)

## 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.