@misc{01M3VSZ8G0WR162P5E3ETMCF23,
  abstract     = {{Outer-hair-cell (OHC) loss is a primary deficit of sensorineural hearing loss (SNHL), impairing cochlear amplification and frequency selectivity and thereby elevating hearing thresholds. Biophysically-inspired DNN-based hearing-aid (HA) algorithms have been proposed to compensate for OHC deficits and have shown clear benefits in objective speech intelligibility and quality metrics (e.g. HASPI, HASQI). However, comprehensive subjective validation of these benefits in human listeners is still missing. In this work, we present a subjective evaluation of a biophysically-inspired HA model targeting OHC deficits. The cochlear module of an auditory model was individualized based on each listener’s pure-tone audiogram and integrated into a trainable system, which includes the personalized model and a normal-hearing reference model, and the resulting trained HA was evaluated using a Matrix test comparing intelligibility scores for unprocessed and HA-processed noisy speech. The results revealed a significant benefit of the HA model over the unprocessed condition in the range of +1 to +27%, providing behavioral confirmation of the efficacy of the HA model. This study closes the gap between objective and perceptual evidence for this new generation of DNN-based HA algorithms, paving the way for their integration into next-generation DNN-accelerated chips for hearables and hearing aids.}},
  author       = {{Wen, Chuan and Nissens, Brent and De Poortere, Nele and Thienpont, Morgan and Inghels, Matthias and Fráter, Attila and Torfs, Guy and Verhulst, Sarah}},
  keywords     = {{Hearing loss compensation,speech intelligibility,DNN biophysically-inspired hearing aids}},
  language     = {{eng}},
  pages        = {{5}},
  series       = {{arXiv}},
  title        = {{Subjective evaluation of DNN and auditory-model-based hearing-loss compensation}},
  url          = {{http://doi.org/10.48550/arXiv.2609.28033}},
  year         = {{2026}},
}

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