Current hearing aids normally provide amplification based on a general prescriptive fitting, and the benefits provided by the hearing aids vary among different listening environments despite the inclusion of noise suppression feature. Motivated by this fact, this paper proposes a data-driven machine learning technique to develop hearing aid fittings that are customised to speech in different noisy environments. A differentiable hearing loss model is proposed and used to optimise fittings with back-propagation. The customisation is reflected on the data of speech in different noise with also the consideration of noise suppression. The objective evaluation shows the advantages of optimised custom fittings over general prescriptive fittings.

翻译：目前的助听器通常提供基于一般规范装配的扩音,助听器所提供的好处因听力环境不同而各有不同,尽管纳入了抑制噪音功能。由于这一事实,本文件提议采用数据驱动的机器学习技术,开发适合在不同吵闹环境中说话的助听器。提出了不同的听力损失模型,并用来优化后方适应装置。定制反映在不同噪音中的语音数据中,同时也考虑到抑制噪音。客观评估显示,优化的自定义装配比一般规范装配的优点。