A multi-modal streaming approach to understanding aided speech perception in noise: discovering relationships between device limitations, perceptual abilities, and neuro-modulation of feature coding

项目摘要/摘要 据估计，只有大约七分之一的美国听力损失患者使用助听器。 尽管人们普遍认为助听器等设备可以改善听力健康和质量 生活的一部分。采用率低的最常见原因之一是，当尝试遵循 在有噪音的情况下交谈。为了解决这个问题，大多数保费都采用了自适应方向 处理(DP)，在被认为不太相关的位置降低增益，从而提高信噪比 (SNR)更相关的演讲。然而，这种策略容易受到处理延迟和不准确的影响 语音本地化，尽管HA DP已经假定了好处，但与之相关的神经机制并不是 很好理解。人们期望通过提高信噪比来支持声学特征表示； 然而，如果听者的语音定位在噪声中较差，并且HA DP延迟或不准确，则感知能力较差- 特征表示(例如，空间)可能会阻止有效的听觉流传输。过去的调查已经评估了 DP相对于无辅助或全向扩增有好处，并且假设了准确的适应或 使用了固定的方向聚焦。此外，好处通常是通过自我报告、倾听努力或句子来衡量的 噪声中的识别。这种传统的方法有重要的局限性：1)研究缺乏对HA的系统控制 DP，因为制造商设置了障碍，以及2)结果仅提供了机制的间接测量 对推定的利益负责。相反，我们在这里将受控HA DP评估链接到强大的 建立了听觉客体注意的理论模型。通过这样做，我们可以直接关联福利 具有相关神经机制的HA DP，我们可以更准确地针对未来HA的创新努力 技术我们的多模式流方法包括1)系统地控制HA DP，2)分析 基本感知能力，以及3)支持听觉串流的神经处理措施。在目标1中，我们 在新的噪声中语音任务期间参数地改变增益降低的空间和时间精度，以 直接评估焦点和时间转换到自由场中的语音位置的影响(实验。1)。在目标2中， 我们使用我们的多模流方法来测量助听器对空间调谐曲线的影响 被动倾听时的锐度(Exp.2a)以及在处理空间位置时(实验2B)。观察到的 然后，针对被确定为潜在的心理物理措施，分析DP策略的好处 HA-DP的预测因子(目标3)。这项研究的结果将提供对辅助言语的新的理解 在噪声中的感知和指导未来的HA发展植根于已建立的听觉流传输理论。 计划中的R01研究将进一步研究辅助声学和感知特征编码以及听觉 流入一种基于听觉对象的注意力模型。

PROJECT SUMMARY/ABSTRACT It is estimated that only about 1 in 7 Americans who have hearing loss use some hearing assistive device despite the generally accepted view that devices such as hearing aids (HAs) improve hearing health and quality of life. Among the most common reasons for low adoption is an unclear benefit when trying to follow a conversation in the presence of noise. To address this problem, most premium HAs employ adaptive directional processing (DP) that reduces gain at locations considered less relevant, thus improving signal-to-noise ratios (SNRs) of more relevant speech. This strategy, however, is susceptible to processing delays and inaccurate speech localization, and though HA DP has assumed benefits, the neural mechanisms associated with it are not well understood. One would expect that HAs support acoustic-feature representation by increasing SNR; however, if a listener's speech localization is poor in noise and HA DP is delayed or inaccurate, poor perceptual- feature representation (e.g., space) may prevent effective auditory streaming. Past investigations have assessed DP benefits relative to unaided or omnidirectional amplification and have either assumed accurate adaptation or used a fixed directional focus. Moreover, benefit is typically gauged by self-report, listening effort, or sentence recognition in noise. This traditional approach has important limitations: 1) studies lack systematic control of HA DP because of manufacturer-placed barriers, and 2) results provide only indirect measures of the mechanisms responsible for the presumed advantages. In contrast, here we link controlled HA DP evaluation to a strong and established theoretical model of auditory object-based attention. In doing so, we can directly associate benefits of HA DP with relevant neural mechanisms, and we can more precisely target innovation efforts for future HA technology. Our multi-modal streaming approach incorporates 1) systematic control of HA DP, 2) an assay of basic perceptual abilities, and 3) measures of neural processing that support auditory streaming. In Aim 1, we parametrically vary the spatial and temporal precision of gain reduction during a novel speech-in-noise task, to assess directly the effects of focal and timelier transitions to speech location in the free field (Exp. 1). In Aim 2, we use our multi-modal streaming approach to measure the effect of hearing aids on spatial tuning curve sharpness both when passively listening (Exp. 2a) and when attending to a spatial location (Exp. 2b). Observed benefits in DP strategies are then analyzed with respect to psychophysical measures identified as potential predictors of HA DP (Aim 3). The results of this study will deliver a novel understanding of aided speech perception in noise and guide future HA development grounded in an established theory of auditory streaming. Planned R01 research will futher investigate aided acoustic and perceptual feature coding and auditory streaming in an auditory-object based model of attention.