In this study, a novel human acoustic perception motivated Wiener filter speech enhancement system is presented to cope with real-world interfering background noises. Guiding by the speech presence probability, two alternative methods are proposed to reduce the noise by adopting the audible sound pressure level (SPL) and the masking characteristic of the human auditory system to achieve better listening comfort level. More specifically, when the probability of speech presence in the noisy signal is less than the decision threshold, a new SPL compressed method effectively reduces the noise. When the speech presence probability is more than the decision threshold, an improved acoustical mask threshold constrained Wiener filter approach enhances the noisy speech. Moreover, in order to evaluate the performance of the new system, the proposed algorithm is compared with the classic prior signal-to-noise ratio-based Wiener filter and three acoustic perception related algorithms. The experimental results show that the proposed algorithm significantly outperforms the four comparing algorithms in terms of speech quality and intelligibility either in stationary or moderate non-stationary noisy environments. Thus, the intended approach can be employed as the front-end module for various speech-related applications.

中文翻译：

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基于人类听觉的概率决策驱动语音增强算法

在这项研究中，提出了一种新颖的以人类听觉为动力的维纳滤波器语音增强系统，以应对现实世界中的干扰背景噪声。以语音存在概率为指导，提出了两种替代方法，通过采用可听声压级（SPL）和人类听觉系统的掩蔽特性来降低噪声，以达到更好的聆听舒适度。更具体地，当在噪声信号中语音存在的概率小于判定阈值时，新的SPL压缩方法有效地降低了噪声。当语音存在概率大于决策阈值时，改进的声学蒙版阈值约束维纳滤波器方法会增强嘈杂的语音。此外，为了评估新系统的性能，将该算法与经典的基于先验信噪比的维纳滤波器和三种与听觉相关的算法进行了比较。实验结果表明，无论是在固定的还是中等的非平稳噪声环境下，该算法在语音质量和清晰度方面均明显优于四种比较算法。因此，可以将预期的方法用作各种语音相关应用程序的前端模块。