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arXiv · 2507.15396

Neuro-MSBG: An End-to-End Neural Model for Hearing Loss Simulation

Hui-Guan Yuan·Ryandhimas E. Zezario·Shafique Ahmed·Hsin-Min Wang·Kai-Lung Hua·Yu Tsao
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Abstract

Hearing loss simulation models are essential for hearing aid deployment. However, existing models have high computational complexity and latency, which limits real-time applications and lack direct integration with speech processing systems. To address these issues, we propose Neuro-MSBG, a lightweight end-to-end model with a personalized audiogram encoder for effective time-frequency modeling. Experiments show that Neuro-MSBG supports parallel inference and retains the intelligibility and perceptual quality of the original MSBG, with a Spearman's rank correlation coefficient (SRCC) of 0.9247 for Short-Time Objective Intelligibility (STOI) and 0.8671 for Perceptual Evaluation of Speech Quality (PESQ). Neuro-MSBG reduces simulation runtime by a factor of 46 (from 0.970 seconds to 0.021 seconds for a 1 second input), further demonstrating its efficiency and practicality.

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Hui-Guan Yuan, Ryandhimas E. Zezario, Shafique Ahmed, Hsin-Min Wang, Kai-Lung Hua, Yu Tsao. 2025-07-21. Neuro-MSBG: An End-to-End Neural Model for Hearing Loss Simulation. https://arxiv.org/abs/2507.15396

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