It is essential but quite challenging to alleviate speech information loss and distortion while developing the speech processing algorithms in hearing aids. Recently, many speech enhancement methods based on deep learning are proven effective. However, most of the algorithms fail to achieve real-time processing, which is significant for hearing aids, especially for a smartphone-centered binaural hearing aid system. A supervised speech enhancement method based on an RNN structure is proposed to address the real-time problem. The problem is explored as a resource-constrained speech intelligibility improvement problem with the target of improving speech intelligibility at low SNR situations. The objective experimental result using the standard evaluation metrics has verified the superiority of the proposed method. The trial use by a small number of volunteers also indicates that the user experience has been improved with the proposed method.

中文翻译：

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基于智能手机的双耳助听器的监督语音增强方法


在开发助听器中的语音处理算法时，减轻语音信息丢失和失真是必要且具有挑战性的。近年来，许多基于深度学习的语音增强方法被证明是有效的。然而，大多数算法无法实现实时处理，这对于助听器尤其是以智能手机为中心的双耳助听系统来说意义重大。为了解决实时性问题，提出了一种基于RNN结构的有监督语音增强方法。该问题被探索为资源受限的语音清晰度改进问题，其目标是在低信噪比情况下提高语音清晰度。使用标准评价指标的客观实验结果验证了所提方法的优越性。少数志愿者的试用也表明该方法的用户体验得到了改善。