Improving intelligibility in noise for hearing-impaired listeners

项目摘要 听力受损(HI)听众的主要抱怨是当背景噪音时言语理解能力差 (见Dillon，2012)。因此，这个问题可以被认为是对估计的 3750万美国听力损失患者(NIDCD，2015)。因此，这个问题的解决方案通常具有 被认为是我们领域的“圣杯”。一种建议的解决方案涉及单麦克风算法，以 从背景噪声中提取语音。这可能被认为是最终目标，因为它是算法 它执行侦听器无法执行的任务。但是，尽管世界各地的团体进行了50年的努力， 能够提高清晰度的算法，特别是对于HI听众来说，仍然难以捉摸。我们有 最近首次演示了一种能够在噪声中明显改善HI的算法 听众(Healy等人，2013b、2014、2015)。这项工作不仅具有开创性，而且提高了可理解性 是相当可观的。在算法处理之前，我们的大多数HI听众能够理解大约1 in 在嘈杂的句子中每3个单词，有些分数低至0-10%。跟随算法 处理过程中，我们的许多HI听众的清晰度提高到了大约90%。中国的长期目标是 目前提出的研究是为了提高我们解决高音听者的噪声中语音问题的能力。这个 第一个目标是建立我们理解噪声中语音识别所必需的基本信息。在.期间 为了达到这个目的，我们为每个单独的频率区域建立了我们所说的“噪声敏感性”。 演讲。我们在这里认为，目前的努力混淆了噪声易感性与语音频带重要性，因此 噪声敏感性尚不清楚。然后，我们通过以下方式提供这种知识的直接和即时应用 将噪声敏感性纳入语音清晰度指数的修正系数(ANSI，1997)。在.期间 第二和第三个目标，我们通过改进我们的算法在基础和 重要的方式。在目标2中，我们建立了一个新的进步，最大化语音信息，同时 将噪音降至最低。我们通过将我们对噪声易感性和语音的理解结合在一起来实现这一点 在我们的算法中加入频带重要性。在目标3中，我们比较了所得到的清晰度和音质 来自我们的算法的三个不同的基本方案。其中一个方案是新颖的，将在这里介绍。 它承诺提供我们已经实施的两个计划的优点。总体而言，目前的研究 有可能改变我们对噪声中语音识别的基本理解，并提高ANSI 用于预测它的标准。此外，拟议的研究是翻译的，并解决了以下主要限制 听众们，大家好。我们通过将我们的算法在重要的和基本的 因此，我们更接近于将其应用于助听器和人工耳蜗的最终目标。 这里描述的贡献有可能对数百万人的生活质量产生重大影响 并改变我们对听力损失的治疗。

Project Summary The primary complaint of hearing-impaired (HI) listeners is poor speech understanding when background noise is present (see Dillon, 2012). This problem can therefore be considered the most significant for the estimated 37.5 million Americans with hearing loss (NIDCD, 2015). Accordingly, a solution to this problem has commonly been considered a “holy grail” of our field. One proposed solution involves a single-microphone algorithm to extract speech from background noise. This may be considered an ultimate goal, because it is the algorithm that performs the task that the listener cannot. But despite 50 years of effort by groups around the world, an algorithm capable of improving intelligibility, especially for HI listeners, has remained elusive. We have recently provided the first demonstration of an algorithm capable of improving intelligibly in noise for HI listeners (Healy et al., 2013b, 2014, 2015). Not only is this work seminal, but the intelligibility improvements are substantial. Prior to algorithm processing, most of our HI listeners were able to understand roughly 1 in every 3 words within noisy sentences, and some scores were as low as 0-10%. Following algorithm processing, intelligibility for many of our HI listeners improved to roughly 90%. The long-term goal of the currently proposed study is to advance our ability to remedy the speech-in-noise problem for HI listeners. The first aim establishes basic information essential to our understanding of speech recognition in noise. During this aim, we establish what we have termed “noise susceptibility” for each individual frequency region of speech. We argue here that current efforts confound noise susceptibility with speech band importance, so that noise susceptibility is not known. We then provide direct and immediate application of this knowledge through a correction factor to incorporate noise susceptibility into the Speech Intelligibility Index (ANSI, 1997). During the second and third aims, we provide translational significance by advancing our algorithm in fundamental and important ways. During Aim 2, we establish a novel advancement that maximizes speech information while minimizing noise. We accomplish this by incorporating our understanding of noise susceptibility and speech band importance into our algorithm. During Aim 3, we compare the intelligibility and sound quality resulting from three different foundational schemes for our algorithm. One scheme is novel and will be introduced here. It promises to offer the advantages of both schemes we have already implemented. Overall, the current study has the potential to transform our basic understanding of speech recognition in noise and improve the ANSI standard used to predict it. Further, the proposed study is translational and addresses the primary limitation of HI listeners. We address this highly significant issue by advancing our algorithm in important and fundamental ways, thus progressing closer to our ultimate goal of implementation into hearing aids and cochlear implants. The contributions described here have the potential to substantially impact quality of life for millions of Americans and transform our treatment of hearing loss.