This work presents a single-channel speech enhancement (SE) framework based on the super-Gaussian extension of the joint maximum a posteriori (SGJMAP) estimation rule. The developed SE algorithm is an open-source research smartphone-based application for hearing improvement studies. In this algorithm, the SGJMAP-based estimation for noisy speech mixture is smoothed along the frequency axis by a Mel filter-bank, resulting in a Mel-warped frequency-domain SGJMAP estimation. The impulse response of this Mel-warped estimation is obtained by applying a Mel-warped inverse discrete cosine transform (Mel-IDCT). This helps in filtering out the background noise and enhancing the speech signal. The proposed application is implemented on an iPhone (Apple, Cupertino, CA) to operate in real time and tested with normal-hearing (NH) and hearing-impaired (HI) listeners with different types of hearing aids through wireless connectivity. The objective speech quality and intelligibility test results are used to compare the performance of the proposed algorithm to existing conventional single-channel SE methods. Additionally, test results from NH and HI listeners show substantial improvement in speech recognition with the developed method in simulated real-world noisy conditions at different signal-to-noise ratio levels.

中文翻译：

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基于智能手机的助听器单通道语音增强应用

这项工作提出了一个基于联合最大后验的超高斯扩展的单通道语音增强（SE）框架(SGJMAP) 估计规则。开发的 SE 算法是一种基于智能手机的开源研究应用程序，用于听力改善研究。在该算法中，基于 SGJMAP 的噪声语音混合估计通过 Mel 滤波器组沿频率轴平滑，从而产生 Mel 扭曲频域 SGJMAP 估计。通过应用 Mel-warped 逆离散余弦变换 (Mel-IDCT) 获得该 Mel-warped 估计的脉冲响应。这有助于滤除背景噪声并增强语音信号。提议的应用程序在 iPhone（Apple，Cupertino，CA）上实现实时操作，并通过无线连接对具有不同类型助听器的听力正常 (NH) 和听力受损 (HI) 听众进行测试。客观的语音质量和可懂度测试结果用于比较所提出算法与现有传统单通道 SE 方法的性能。此外，来自 NH 和 HI 听众的测试结果表明，在不同信噪比水平的模拟现实世界嘈杂条件下，使用开发的方法可以显着改善语音识别。