The intelligibility and quality of speech from a mobile phone or public announcement system are often affected by background noise in the listening environment. By pre-processing the speech signal it is possible to improve the speech intelligibility and quality -- this is known as near-end listening enhancement (NLE). Although, existing NLE techniques are able to greatly increase intelligibility in harsh noise environments, in favorable noise conditions the intelligibility of speech reaches a ceiling where it cannot be further enhanced. Actually, the focus of existing methods solely on improving the intelligibility causes unnecessary processing of the speech signal and leads to speech distortions and quality degradations. In this paper, we provide a new rationale for NLE, where the target speech is minimally processed in terms of a processing penalty, provided that a certain performance constraint, e.g., intelligibility, is satisfied. We present a closed-form solution for the case where the performance criterion is an intelligibility estimator based on the approximated speech intelligibility index and the processing penalty is the mean-square error between the processed and the clean speech. This produces an NLE method that adapts to changing noise conditions via a simple gain rule by limiting the processing to the minimum necessary to achieve a desired intelligibility, while at the same time focusing on quality in favorable noise situations by minimizing the amount of speech distortions. Through simulation studies, we show the proposed method attains speech quality on par or better than existing methods in both objective measurements and subjective listening tests, whilst still sustaining objective speech intelligibility performance on par with existing methods.

翻译：手机或公共广播系统发出的语音，其清晰度和质量常受收听环境中的背景噪声影响。通过预处理语音信号，可以提升语音清晰度和质量——这被称为近端听感增强（NLE）。尽管现有NLE技术能在恶劣噪声环境下大幅提高清晰度，但在有利噪声条件下，语音清晰度会达到一个无法进一步提升的上限。实际上，现有方法仅聚焦于提升清晰度，导致对语音信号的不必要处理，并引发语音失真与质量下降。本文提出一种新的NLE原则：在满足特定性能约束（如清晰度）的前提下，根据处理代价对目标语音进行最小化处理。我们给出一个闭式解，其中性能标准是基于近似语音清晰度指数的清晰度估计器，处理代价则是处理后的语音与纯净语音之间的均方误差。由此产生的NLE方法通过简单的增益规则适应变化的噪声条件——将处理限制在实现目标清晰度所需的最低限度，同时在有利噪声情境中通过最小化语音失真来聚焦质量。仿真研究表明，所提方法在客观测量和主观听音测试中均能达到与现有方法相当或更优的语音质量，同时保持与现有方法相当的客观语音清晰度性能。