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

2024-03-17

IEEE Access

AI Summary

Cochlear implants (CIs) suffer from poor speech recognition accuracy and degraded sound quality in complex acoustic environments—such as multi-talker scenes and high-noise conditions. To address this, this work presents a systematic review of AI-driven CI speech recognition (CI-ASR) and speech enhancement techniques, proposing the first unified methodology framework encompassing CI-specific acoustic modeling, biologically constrained representations, self-supervised learning, DPRNN-based source separation, and noise-robust training. We survey standard evaluation metrics and CI-exclusive datasets, and comparatively analyze CNN-, RNN-, and Transformer-based architectures. Results show that state-of-the-art AI approaches reduce word error rate under noise to 18.3%, substantially outperforming conventional digital signal processing (DSP) methods while improving both speech intelligibility and naturalness. The study identifies explainable AI and low-latency embedded deployment as critical unsolved challenges, thereby providing theoretical foundations and technical pathways for next-generation intelligent CIs.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Speech
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications

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 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.

Problem

• Artificial Intelligence
• Cochlear Implants
• Speech Recognition

Innovation

• Artificial Intelligence
• Cochlear Implants
• Automatic Speech Recognition