Personalized VR for hearing research with embedded devices

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 often trained and evaluated in simplified acoustic scenarios, neglecting multimodal cues, user interaction, environmental dynamics, and the strict latency and power constraints of embedded devices. As a result, a persistent gap remains between algorithmic performance and everyday listening experience. This position paper reviews recent progress in speech enhancement, embedded Artificial Intelligence hardware, and hearing aid systems, and argues for a shift toward ecologically valid evaluation and hardware-aware design. We propose virtual reality as a reproducible, multisensory benchmarking platform enabling joint assessment of human perception and algorithmic processing. This perspective outlines a research roadmap toward adaptive, context-aware, and practically deployable hearing technologies.