Hearing aids in the era of foundation models

The recent introduction of foundation models (FMs) has taken the world by storm. Ranging from large language models (LLMs) to image and audio analysis and generation, FMs have introduced a new paradigm in artificial intelligence (AI), one where practitioners transition from standard supervised machine learning to prompting and in-context learning. This has implications for hearing aid research, and specifically for the use of such models for noise attenuation and speech enhancement. Even though the uptake of FMs is minimal to non-existent for this application domain, mainly due to the prohibitive computational complexity of those models, there are nevertheless ways to benefit from FM advances in an indirect way. We review these approaches in the present contribution.