AES Europe 2026

Deep learning has significantly improved speech enhancement
performance in controlled laboratory conditions, yet these
advances rarely translate into robust real-world benefit
for hearing aid users. Current algorithms are trained;
evaluated in simplified acoustic scenarios, neglecting
multimodal cues, user interaction, environmental dynamics,
; the strict latency; power constraints of embedded
devices. As a result, a persistent gap remains between
algorithmic performance; everyday listening experience.
This position paper reviews recent progress in speech
enhancement, embedded Artificial Intelligence hardware,;
hearing aid systems,; argues for a shift toward
ecologically valid evaluation; hardware-aware design. We
propose virtual reality as a reproducible, multisensory
benchmarking platform enabling joint assessment of human
perception; algorithmic processing. This perspective
outlines a research roadmap toward adaptive, context-aware,
; practically deployable hearing technologies.